{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation,Dropout\n",
    "from keras import optimizers\n",
    "from sklearn.datasets import load_iris   #导入数据集iris\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_data=datasets.load_iris()\n",
    "input_data=iris_data.data\n",
    "correct = iris_data.target\n",
    "\n",
    "#对输入数据进行标准化处理\n",
    "ave_input=np.average(input_data,axis=0)\n",
    "std_input=np.std(input_data,axis=0)\n",
    "input_data=(input_data-ave_input)/std_input\n",
    "#将正确答案转换成独热编码格式\n",
    "n_data=len(correct) #样本数量\n",
    "correct_data=np.zeros((n_data,3))\n",
    "for i in range(n_data):\n",
    "    correct_data[i,correct[i]] = 1.0\n",
    "    \n",
    "#分割测试集与训练集\n",
    "index=np.arange(n_data)\n",
    "index_train=index[index%2==0]\n",
    "index_test=index[index%2!=0]\n",
    "input_train=input_data[index_train,:]\n",
    "input_test=input_data[index_test,:]\n",
    "correct_train=correct_data[index_train,:]\n",
    "correct_test=correct_data[index_test,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#SGD\n",
    "from keras import optimizers\n",
    "model = Sequential()\n",
    "model.add(Dense(75, kernel_initializer='uniform', input_shape=(4,)))\n",
    "model.add(Activation('softmax'))\n",
    "sgd = optimizers.SGD()\n",
    "model.compile(loss='categorical_crossentropy', optimizer=sgd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adagrad\n",
    "model_ada= Sequential()\n",
    "model_ada.add(Dense(20,input_dim=4,activation='sigmoid'))\n",
    "# 隐层\n",
    "model_ada.add(Dense(20, activation='sigmoid',input_dim=20))  # Dense层为中间层\n",
    "# model.add(Dropout(0.5))\n",
    "model_ada.add(Dense(20, activation='sigmoid',input_dim=20))  # Dense层为中间层\n",
    "\n",
    "# 输出层\n",
    "model_ada.add(Dense(3, input_dim=20,activation='softmax'))\n",
    "ada=optimizers.Adagrad(learning_rate=0.01)\n",
    "model_ada.compile(loss='categorical_crossentropy', optimizer=ada,metrics=['accuracy'])\n",
    "# model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 127 samples, validate on 23 samples\n",
      "Epoch 1/500\n",
      "127/127 [==============================] - 0s 558us/step - loss: 1.4758 - accuracy: 0.2126 - val_loss: 0.7690 - val_accuracy: 1.0000\n",
      "Epoch 2/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.2419 - accuracy: 0.3071 - val_loss: 0.9422 - val_accuracy: 0.0000e+00\n",
      "Epoch 3/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.1556 - accuracy: 0.3937 - val_loss: 1.0766 - val_accuracy: 0.0000e+00\n",
      "Epoch 4/500\n",
      "127/127 [==============================] - 0s 62us/step - loss: 1.1159 - accuracy: 0.3937 - val_loss: 1.1622 - val_accuracy: 0.0000e+00\n",
      "Epoch 5/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 1.0867 - accuracy: 0.3937 - val_loss: 1.2388 - val_accuracy: 0.0000e+00\n",
      "Epoch 6/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0711 - accuracy: 0.3937 - val_loss: 1.2850 - val_accuracy: 0.0000e+00\n",
      "Epoch 7/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0618 - accuracy: 0.3937 - val_loss: 1.3272 - val_accuracy: 0.0000e+00\n",
      "Epoch 8/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0530 - accuracy: 0.3937 - val_loss: 1.3606 - val_accuracy: 0.0000e+00\n",
      "Epoch 9/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0479 - accuracy: 0.3937 - val_loss: 1.3877 - val_accuracy: 0.0000e+00\n",
      "Epoch 10/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0444 - accuracy: 0.3937 - val_loss: 1.4023 - val_accuracy: 0.0000e+00\n",
      "Epoch 11/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0411 - accuracy: 0.3937 - val_loss: 1.4023 - val_accuracy: 0.0000e+00\n",
      "Epoch 12/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0363 - accuracy: 0.5197 - val_loss: 1.4142 - val_accuracy: 0.0000e+00\n",
      "Epoch 13/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0327 - accuracy: 0.4331 - val_loss: 1.4188 - val_accuracy: 0.0000e+00\n",
      "Epoch 14/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0309 - accuracy: 0.6142 - val_loss: 1.4148 - val_accuracy: 0.0000e+00\n",
      "Epoch 15/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0278 - accuracy: 0.6142 - val_loss: 1.4147 - val_accuracy: 0.0000e+00\n",
      "Epoch 16/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0245 - accuracy: 0.7717 - val_loss: 1.4084 - val_accuracy: 0.0000e+00\n",
      "Epoch 17/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0195 - accuracy: 0.7795 - val_loss: 1.4105 - val_accuracy: 0.0000e+00\n",
      "Epoch 18/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0164 - accuracy: 0.7795 - val_loss: 1.4094 - val_accuracy: 0.0000e+00\n",
      "Epoch 19/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0125 - accuracy: 0.7087 - val_loss: 1.4061 - val_accuracy: 0.0000e+00\n",
      "Epoch 20/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0088 - accuracy: 0.7402 - val_loss: 1.4012 - val_accuracy: 0.0000e+00\n",
      "Epoch 21/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0056 - accuracy: 0.7795 - val_loss: 1.3918 - val_accuracy: 0.0000e+00\n",
      "Epoch 22/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0005 - accuracy: 0.7638 - val_loss: 1.3871 - val_accuracy: 0.0000e+00\n",
      "Epoch 23/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.9964 - accuracy: 0.7717 - val_loss: 1.3843 - val_accuracy: 0.0000e+00\n",
      "Epoch 24/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.9926 - accuracy: 0.6142 - val_loss: 1.3810 - val_accuracy: 0.0000e+00\n",
      "Epoch 25/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.9892 - accuracy: 0.7717 - val_loss: 1.3747 - val_accuracy: 0.0000e+00\n",
      "Epoch 26/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.9826 - accuracy: 0.7638 - val_loss: 1.3651 - val_accuracy: 0.0000e+00\n",
      "Epoch 27/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.9779 - accuracy: 0.7717 - val_loss: 1.3568 - val_accuracy: 0.0000e+00\n",
      "Epoch 28/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.9719 - accuracy: 0.7638 - val_loss: 1.3468 - val_accuracy: 0.0000e+00\n",
      "Epoch 29/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.9677 - accuracy: 0.7480 - val_loss: 1.3369 - val_accuracy: 0.0000e+00\n",
      "Epoch 30/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.9624 - accuracy: 0.7559 - val_loss: 1.3321 - val_accuracy: 0.0000e+00\n",
      "Epoch 31/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.9557 - accuracy: 0.7638 - val_loss: 1.3238 - val_accuracy: 0.0000e+00\n",
      "Epoch 32/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.9492 - accuracy: 0.7717 - val_loss: 1.3119 - val_accuracy: 0.0000e+00\n",
      "Epoch 33/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.9437 - accuracy: 0.7638 - val_loss: 1.3036 - val_accuracy: 0.0000e+00\n",
      "Epoch 34/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.9364 - accuracy: 0.7638 - val_loss: 1.2953 - val_accuracy: 0.0000e+00\n",
      "Epoch 35/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.9329 - accuracy: 0.7480 - val_loss: 1.2871 - val_accuracy: 0.0000e+00\n",
      "Epoch 36/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.9233 - accuracy: 0.7638 - val_loss: 1.2794 - val_accuracy: 0.0000e+00\n",
      "Epoch 37/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.9161 - accuracy: 0.7638 - val_loss: 1.2677 - val_accuracy: 0.0000e+00\n",
      "Epoch 38/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.9098 - accuracy: 0.7717 - val_loss: 1.2576 - val_accuracy: 0.0000e+00\n",
      "Epoch 39/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.9013 - accuracy: 0.7638 - val_loss: 1.2463 - val_accuracy: 0.0000e+00\n",
      "Epoch 40/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.8940 - accuracy: 0.7717 - val_loss: 1.2371 - val_accuracy: 0.0000e+00\n",
      "Epoch 41/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.8873 - accuracy: 0.7717 - val_loss: 1.2222 - val_accuracy: 0.0000e+00\n",
      "Epoch 42/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.8780 - accuracy: 0.7717 - val_loss: 1.2134 - val_accuracy: 0.0000e+00\n",
      "Epoch 43/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.8704 - accuracy: 0.7717 - val_loss: 1.2032 - val_accuracy: 0.0000e+00\n",
      "Epoch 44/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.8619 - accuracy: 0.7638 - val_loss: 1.1932 - val_accuracy: 0.0000e+00\n",
      "Epoch 45/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.8541 - accuracy: 0.7717 - val_loss: 1.1859 - val_accuracy: 0.0000e+00\n",
      "Epoch 46/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.8452 - accuracy: 0.7717 - val_loss: 1.1746 - val_accuracy: 0.0000e+00\n",
      "Epoch 47/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.8371 - accuracy: 0.7717 - val_loss: 1.1632 - val_accuracy: 0.0000e+00\n",
      "Epoch 48/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.8284 - accuracy: 0.7717 - val_loss: 1.1554 - val_accuracy: 0.0000e+00\n",
      "Epoch 49/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.8203 - accuracy: 0.7717 - val_loss: 1.1472 - val_accuracy: 0.0000e+00\n",
      "Epoch 50/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.8107 - accuracy: 0.7717 - val_loss: 1.1369 - val_accuracy: 0.0000e+00\n",
      "Epoch 51/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.8018 - accuracy: 0.7717 - val_loss: 1.1259 - val_accuracy: 0.0000e+00\n",
      "Epoch 52/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.7930 - accuracy: 0.7717 - val_loss: 1.1159 - val_accuracy: 0.0000e+00\n",
      "Epoch 53/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.7842 - accuracy: 0.7717 - val_loss: 1.1088 - val_accuracy: 0.0000e+00\n",
      "Epoch 54/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.7755 - accuracy: 0.7795 - val_loss: 1.0984 - val_accuracy: 0.0000e+00\n",
      "Epoch 55/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.7672 - accuracy: 0.7717 - val_loss: 1.0904 - val_accuracy: 0.0000e+00\n",
      "Epoch 56/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.7579 - accuracy: 0.7717 - val_loss: 1.0799 - val_accuracy: 0.0000e+00\n",
      "Epoch 57/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.7489 - accuracy: 0.7795 - val_loss: 1.0725 - val_accuracy: 0.0000e+00\n",
      "Epoch 58/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.7398 - accuracy: 0.7795 - val_loss: 1.0651 - val_accuracy: 0.0000e+00\n",
      "Epoch 59/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.7317 - accuracy: 0.7717 - val_loss: 1.0579 - val_accuracy: 0.0000e+00\n",
      "Epoch 60/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.7245 - accuracy: 0.7874 - val_loss: 1.0476 - val_accuracy: 0.0000e+00\n",
      "Epoch 61/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.7150 - accuracy: 0.7874 - val_loss: 1.0400 - val_accuracy: 0.0000e+00\n",
      "Epoch 62/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.7063 - accuracy: 0.7874 - val_loss: 1.0346 - val_accuracy: 0.0000e+00\n",
      "Epoch 63/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.6981 - accuracy: 0.7874 - val_loss: 1.0297 - val_accuracy: 0.0000e+00\n",
      "Epoch 64/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.6906 - accuracy: 0.7874 - val_loss: 1.0239 - val_accuracy: 0.0000e+00\n",
      "Epoch 65/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.6827 - accuracy: 0.7874 - val_loss: 1.0172 - val_accuracy: 0.0000e+00\n",
      "Epoch 66/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 0.6749 - accuracy: 0.7874 - val_loss: 1.0112 - val_accuracy: 0.0000e+00\n",
      "Epoch 67/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.6667 - accuracy: 0.7874 - val_loss: 1.0064 - val_accuracy: 0.0000e+00\n",
      "Epoch 68/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.6598 - accuracy: 0.7874 - val_loss: 1.0010 - val_accuracy: 0.0000e+00\n",
      "Epoch 69/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.6525 - accuracy: 0.7874 - val_loss: 0.9963 - val_accuracy: 0.0000e+00\n",
      "Epoch 70/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.6455 - accuracy: 0.7874 - val_loss: 0.9898 - val_accuracy: 0.0000e+00\n",
      "Epoch 71/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.6379 - accuracy: 0.7874 - val_loss: 0.9868 - val_accuracy: 0.0000e+00\n",
      "Epoch 72/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.6313 - accuracy: 0.7874 - val_loss: 0.9844 - val_accuracy: 0.0000e+00\n",
      "Epoch 73/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.6251 - accuracy: 0.7874 - val_loss: 0.9807 - val_accuracy: 0.0000e+00\n",
      "Epoch 74/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.6188 - accuracy: 0.7874 - val_loss: 0.9796 - val_accuracy: 0.0000e+00\n",
      "Epoch 75/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.6122 - accuracy: 0.7874 - val_loss: 0.9755 - val_accuracy: 0.0000e+00\n",
      "Epoch 76/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.6062 - accuracy: 0.7874 - val_loss: 0.9733 - val_accuracy: 0.0000e+00\n",
      "Epoch 77/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 0.6001 - accuracy: 0.7874 - val_loss: 0.9705 - val_accuracy: 0.0000e+00\n",
      "Epoch 78/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.5941 - accuracy: 0.7874 - val_loss: 0.9655 - val_accuracy: 0.0000e+00\n",
      "Epoch 79/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.5886 - accuracy: 0.7874 - val_loss: 0.9643 - val_accuracy: 0.0000e+00\n",
      "Epoch 80/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5830 - accuracy: 0.7874 - val_loss: 0.9626 - val_accuracy: 0.0000e+00\n",
      "Epoch 81/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.5781 - accuracy: 0.7874 - val_loss: 0.9569 - val_accuracy: 0.0000e+00\n",
      "Epoch 82/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.5722 - accuracy: 0.7874 - val_loss: 0.9555 - val_accuracy: 0.0000e+00\n",
      "Epoch 83/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5669 - accuracy: 0.7874 - val_loss: 0.9534 - val_accuracy: 0.0000e+00\n",
      "Epoch 84/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5628 - accuracy: 0.7874 - val_loss: 0.9507 - val_accuracy: 0.0000e+00\n",
      "Epoch 85/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5587 - accuracy: 0.7874 - val_loss: 0.9468 - val_accuracy: 0.0000e+00\n",
      "Epoch 86/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 0.5528 - accuracy: 0.7874 - val_loss: 0.9432 - val_accuracy: 0.0000e+00\n",
      "Epoch 87/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.5483 - accuracy: 0.7874 - val_loss: 0.9402 - val_accuracy: 0.0000e+00\n",
      "Epoch 88/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5438 - accuracy: 0.7874 - val_loss: 0.9397 - val_accuracy: 0.0000e+00\n",
      "Epoch 89/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.5398 - accuracy: 0.7874 - val_loss: 0.9389 - val_accuracy: 0.0000e+00\n",
      "Epoch 90/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.5357 - accuracy: 0.7874 - val_loss: 0.9377 - val_accuracy: 0.0000e+00\n",
      "Epoch 91/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.5313 - accuracy: 0.7874 - val_loss: 0.9365 - val_accuracy: 0.0000e+00\n",
      "Epoch 92/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5280 - accuracy: 0.7874 - val_loss: 0.9354 - val_accuracy: 0.0000e+00\n",
      "Epoch 93/500\n",
      "127/127 [==============================] - 0s 23us/step - loss: 0.5242 - accuracy: 0.7874 - val_loss: 0.9325 - val_accuracy: 0.0000e+00\n",
      "Epoch 94/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 0.5201 - accuracy: 0.7874 - val_loss: 0.9330 - val_accuracy: 0.0000e+00\n",
      "Epoch 95/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 0.5166 - accuracy: 0.7874 - val_loss: 0.9328 - val_accuracy: 0.0000e+00\n",
      "Epoch 96/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.5145 - accuracy: 0.7874 - val_loss: 0.9326 - val_accuracy: 0.0000e+00\n",
      "Epoch 97/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5099 - accuracy: 0.7874 - val_loss: 0.9307 - val_accuracy: 0.0000e+00\n",
      "Epoch 98/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.5061 - accuracy: 0.7874 - val_loss: 0.9283 - val_accuracy: 0.0000e+00\n",
      "Epoch 99/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.5028 - accuracy: 0.7874 - val_loss: 0.9263 - val_accuracy: 0.0000e+00\n",
      "Epoch 100/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.4999 - accuracy: 0.7874 - val_loss: 0.9228 - val_accuracy: 0.0000e+00\n",
      "Epoch 101/500\n",
      "127/127 [==============================] - 0s 79us/step - loss: 0.4967 - accuracy: 0.7874 - val_loss: 0.9208 - val_accuracy: 0.0000e+00\n",
      "Epoch 102/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4938 - accuracy: 0.7874 - val_loss: 0.9195 - val_accuracy: 0.0000e+00\n",
      "Epoch 103/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4912 - accuracy: 0.7874 - val_loss: 0.9193 - val_accuracy: 0.0000e+00\n",
      "Epoch 104/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4881 - accuracy: 0.7874 - val_loss: 0.9191 - val_accuracy: 0.0000e+00\n",
      "Epoch 105/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4850 - accuracy: 0.7874 - val_loss: 0.9172 - val_accuracy: 0.0000e+00\n",
      "Epoch 106/500\n",
      "127/127 [==============================] - 0s 23us/step - loss: 0.4823 - accuracy: 0.7874 - val_loss: 0.9149 - val_accuracy: 0.0000e+00\n",
      "Epoch 107/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4801 - accuracy: 0.7874 - val_loss: 0.9138 - val_accuracy: 0.0000e+00\n",
      "Epoch 108/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4772 - accuracy: 0.7874 - val_loss: 0.9141 - val_accuracy: 0.0000e+00\n",
      "Epoch 109/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4749 - accuracy: 0.7874 - val_loss: 0.9125 - val_accuracy: 0.0000e+00\n",
      "Epoch 110/500\n",
      "127/127 [==============================] - 0s 70us/step - loss: 0.4726 - accuracy: 0.7874 - val_loss: 0.9091 - val_accuracy: 0.0000e+00\n",
      "Epoch 111/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 0.4699 - accuracy: 0.7874 - val_loss: 0.9091 - val_accuracy: 0.0000e+00\n",
      "Epoch 112/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4676 - accuracy: 0.7874 - val_loss: 0.9059 - val_accuracy: 0.0000e+00\n",
      "Epoch 113/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4651 - accuracy: 0.7874 - val_loss: 0.9041 - val_accuracy: 0.0000e+00\n",
      "Epoch 114/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4632 - accuracy: 0.7874 - val_loss: 0.9012 - val_accuracy: 0.0000e+00\n",
      "Epoch 115/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4606 - accuracy: 0.7874 - val_loss: 0.9016 - val_accuracy: 0.0000e+00\n",
      "Epoch 116/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4585 - accuracy: 0.7874 - val_loss: 0.8997 - val_accuracy: 0.0000e+00\n",
      "Epoch 117/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4565 - accuracy: 0.7874 - val_loss: 0.8990 - val_accuracy: 0.0000e+00\n",
      "Epoch 118/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4549 - accuracy: 0.7874 - val_loss: 0.8961 - val_accuracy: 0.0000e+00\n",
      "Epoch 119/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4526 - accuracy: 0.7874 - val_loss: 0.8964 - val_accuracy: 0.0000e+00\n",
      "Epoch 120/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4508 - accuracy: 0.7874 - val_loss: 0.8970 - val_accuracy: 0.0000e+00\n",
      "Epoch 121/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4486 - accuracy: 0.7874 - val_loss: 0.8953 - val_accuracy: 0.0000e+00\n",
      "Epoch 122/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4467 - accuracy: 0.7874 - val_loss: 0.8946 - val_accuracy: 0.0000e+00\n",
      "Epoch 123/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4449 - accuracy: 0.7874 - val_loss: 0.8945 - val_accuracy: 0.0000e+00\n",
      "Epoch 124/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4430 - accuracy: 0.7874 - val_loss: 0.8940 - val_accuracy: 0.0000e+00\n",
      "Epoch 125/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4416 - accuracy: 0.7874 - val_loss: 0.8907 - val_accuracy: 0.0000e+00\n",
      "Epoch 126/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4392 - accuracy: 0.7874 - val_loss: 0.8907 - val_accuracy: 0.0000e+00\n",
      "Epoch 127/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4373 - accuracy: 0.7874 - val_loss: 0.8903 - val_accuracy: 0.0000e+00\n",
      "Epoch 128/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4356 - accuracy: 0.7874 - val_loss: 0.8893 - val_accuracy: 0.0000e+00\n",
      "Epoch 129/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4349 - accuracy: 0.7874 - val_loss: 0.8887 - val_accuracy: 0.0000e+00\n",
      "Epoch 130/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4323 - accuracy: 0.7874 - val_loss: 0.8854 - val_accuracy: 0.0000e+00\n",
      "Epoch 131/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4308 - accuracy: 0.7874 - val_loss: 0.8842 - val_accuracy: 0.0000e+00\n",
      "Epoch 132/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.4291 - accuracy: 0.7874 - val_loss: 0.8839 - val_accuracy: 0.0000e+00\n",
      "Epoch 133/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4281 - accuracy: 0.7874 - val_loss: 0.8837 - val_accuracy: 0.0000e+00\n",
      "Epoch 134/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4263 - accuracy: 0.7874 - val_loss: 0.8836 - val_accuracy: 0.0000e+00\n",
      "Epoch 135/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4249 - accuracy: 0.7874 - val_loss: 0.8810 - val_accuracy: 0.0000e+00\n",
      "Epoch 136/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4229 - accuracy: 0.7874 - val_loss: 0.8787 - val_accuracy: 0.0000e+00\n",
      "Epoch 137/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4213 - accuracy: 0.7874 - val_loss: 0.8761 - val_accuracy: 0.0000e+00\n",
      "Epoch 138/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4200 - accuracy: 0.7874 - val_loss: 0.8746 - val_accuracy: 0.0000e+00\n",
      "Epoch 139/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4186 - accuracy: 0.7874 - val_loss: 0.8748 - val_accuracy: 0.0000e+00\n",
      "Epoch 140/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4169 - accuracy: 0.7874 - val_loss: 0.8722 - val_accuracy: 0.0000e+00\n",
      "Epoch 141/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4154 - accuracy: 0.7874 - val_loss: 0.8706 - val_accuracy: 0.0000e+00\n",
      "Epoch 142/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4147 - accuracy: 0.7874 - val_loss: 0.8689 - val_accuracy: 0.0000e+00\n",
      "Epoch 143/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4129 - accuracy: 0.7874 - val_loss: 0.8672 - val_accuracy: 0.0000e+00\n",
      "Epoch 144/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4111 - accuracy: 0.7874 - val_loss: 0.8657 - val_accuracy: 0.0000e+00\n",
      "Epoch 145/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4099 - accuracy: 0.7874 - val_loss: 0.8632 - val_accuracy: 0.0000e+00\n",
      "Epoch 146/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4089 - accuracy: 0.7874 - val_loss: 0.8605 - val_accuracy: 0.0000e+00\n",
      "Epoch 147/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4090 - accuracy: 0.7874 - val_loss: 0.8595 - val_accuracy: 0.0000e+00\n",
      "Epoch 148/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4061 - accuracy: 0.7874 - val_loss: 0.8576 - val_accuracy: 0.0000e+00\n",
      "Epoch 149/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4056 - accuracy: 0.7874 - val_loss: 0.8579 - val_accuracy: 0.0000e+00\n",
      "Epoch 150/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4033 - accuracy: 0.7874 - val_loss: 0.8557 - val_accuracy: 0.0000e+00\n",
      "Epoch 151/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4021 - accuracy: 0.7874 - val_loss: 0.8537 - val_accuracy: 0.0000e+00\n",
      "Epoch 152/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4008 - accuracy: 0.7874 - val_loss: 0.8533 - val_accuracy: 0.0000e+00\n",
      "Epoch 153/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3995 - accuracy: 0.7874 - val_loss: 0.8505 - val_accuracy: 0.0000e+00\n",
      "Epoch 154/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3982 - accuracy: 0.7874 - val_loss: 0.8492 - val_accuracy: 0.0000e+00\n",
      "Epoch 155/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3974 - accuracy: 0.7874 - val_loss: 0.8457 - val_accuracy: 0.0000e+00\n",
      "Epoch 156/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3958 - accuracy: 0.7874 - val_loss: 0.8438 - val_accuracy: 0.0000e+00\n",
      "Epoch 157/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3946 - accuracy: 0.7874 - val_loss: 0.8421 - val_accuracy: 0.0000e+00\n",
      "Epoch 158/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3937 - accuracy: 0.7874 - val_loss: 0.8419 - val_accuracy: 0.0000e+00\n",
      "Epoch 159/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3921 - accuracy: 0.7874 - val_loss: 0.8410 - val_accuracy: 0.0000e+00\n",
      "Epoch 160/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3911 - accuracy: 0.7874 - val_loss: 0.8399 - val_accuracy: 0.0000e+00\n",
      "Epoch 161/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3899 - accuracy: 0.7874 - val_loss: 0.8380 - val_accuracy: 0.0000e+00\n",
      "Epoch 162/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3887 - accuracy: 0.7953 - val_loss: 0.8377 - val_accuracy: 0.0000e+00\n",
      "Epoch 163/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3883 - accuracy: 0.8031 - val_loss: 0.8381 - val_accuracy: 0.0000e+00\n",
      "Epoch 164/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3867 - accuracy: 0.7953 - val_loss: 0.8364 - val_accuracy: 0.0000e+00\n",
      "Epoch 165/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3850 - accuracy: 0.7953 - val_loss: 0.8343 - val_accuracy: 0.0000e+00\n",
      "Epoch 166/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3840 - accuracy: 0.7953 - val_loss: 0.8323 - val_accuracy: 0.0000e+00\n",
      "Epoch 167/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3828 - accuracy: 0.7953 - val_loss: 0.8303 - val_accuracy: 0.0000e+00\n",
      "Epoch 168/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3820 - accuracy: 0.7953 - val_loss: 0.8268 - val_accuracy: 0.0000e+00\n",
      "Epoch 169/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3809 - accuracy: 0.7953 - val_loss: 0.8239 - val_accuracy: 0.0435\n",
      "Epoch 170/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3798 - accuracy: 0.8031 - val_loss: 0.8208 - val_accuracy: 0.0435\n",
      "Epoch 171/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3785 - accuracy: 0.8031 - val_loss: 0.8186 - val_accuracy: 0.0435\n",
      "Epoch 172/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3774 - accuracy: 0.8031 - val_loss: 0.8164 - val_accuracy: 0.0435\n",
      "Epoch 173/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3761 - accuracy: 0.8110 - val_loss: 0.8156 - val_accuracy: 0.0435\n",
      "Epoch 174/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3751 - accuracy: 0.8110 - val_loss: 0.8150 - val_accuracy: 0.0435\n",
      "Epoch 175/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3741 - accuracy: 0.8110 - val_loss: 0.8131 - val_accuracy: 0.0435\n",
      "Epoch 176/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3728 - accuracy: 0.8110 - val_loss: 0.8111 - val_accuracy: 0.0435\n",
      "Epoch 177/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3720 - accuracy: 0.8110 - val_loss: 0.8098 - val_accuracy: 0.0870\n",
      "Epoch 178/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3712 - accuracy: 0.8110 - val_loss: 0.8070 - val_accuracy: 0.0870\n",
      "Epoch 179/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3698 - accuracy: 0.8189 - val_loss: 0.8065 - val_accuracy: 0.0870\n",
      "Epoch 180/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3686 - accuracy: 0.8110 - val_loss: 0.8044 - val_accuracy: 0.0870\n",
      "Epoch 181/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3675 - accuracy: 0.8268 - val_loss: 0.8024 - val_accuracy: 0.0870\n",
      "Epoch 182/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3663 - accuracy: 0.8268 - val_loss: 0.8009 - val_accuracy: 0.1304\n",
      "Epoch 183/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3663 - accuracy: 0.8268 - val_loss: 0.7977 - val_accuracy: 0.1304\n",
      "Epoch 184/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3645 - accuracy: 0.8189 - val_loss: 0.7953 - val_accuracy: 0.1304\n",
      "Epoch 185/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3635 - accuracy: 0.8425 - val_loss: 0.7940 - val_accuracy: 0.1304\n",
      "Epoch 186/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3624 - accuracy: 0.8425 - val_loss: 0.7931 - val_accuracy: 0.1304\n",
      "Epoch 187/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3620 - accuracy: 0.8425 - val_loss: 0.7929 - val_accuracy: 0.1304\n",
      "Epoch 188/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3602 - accuracy: 0.8425 - val_loss: 0.7905 - val_accuracy: 0.1304\n",
      "Epoch 189/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3592 - accuracy: 0.8504 - val_loss: 0.7892 - val_accuracy: 0.1739\n",
      "Epoch 190/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3581 - accuracy: 0.8504 - val_loss: 0.7866 - val_accuracy: 0.2174\n",
      "Epoch 191/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3574 - accuracy: 0.8504 - val_loss: 0.7850 - val_accuracy: 0.2609\n",
      "Epoch 192/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3566 - accuracy: 0.8504 - val_loss: 0.7831 - val_accuracy: 0.2609\n",
      "Epoch 193/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3553 - accuracy: 0.8504 - val_loss: 0.7808 - val_accuracy: 0.2609\n",
      "Epoch 194/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3548 - accuracy: 0.8740 - val_loss: 0.7808 - val_accuracy: 0.2609\n",
      "Epoch 195/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3535 - accuracy: 0.8661 - val_loss: 0.7795 - val_accuracy: 0.2609\n",
      "Epoch 196/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3520 - accuracy: 0.8583 - val_loss: 0.7774 - val_accuracy: 0.2609\n",
      "Epoch 197/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3517 - accuracy: 0.8583 - val_loss: 0.7740 - val_accuracy: 0.3043\n",
      "Epoch 198/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3500 - accuracy: 0.8819 - val_loss: 0.7718 - val_accuracy: 0.3913\n",
      "Epoch 199/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3498 - accuracy: 0.8504 - val_loss: 0.7677 - val_accuracy: 0.4348\n",
      "Epoch 200/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3488 - accuracy: 0.8898 - val_loss: 0.7666 - val_accuracy: 0.4348\n",
      "Epoch 201/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3471 - accuracy: 0.8898 - val_loss: 0.7656 - val_accuracy: 0.4348\n",
      "Epoch 202/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3463 - accuracy: 0.8898 - val_loss: 0.7626 - val_accuracy: 0.4783\n",
      "Epoch 203/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3451 - accuracy: 0.8898 - val_loss: 0.7619 - val_accuracy: 0.4783\n",
      "Epoch 204/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3441 - accuracy: 0.8898 - val_loss: 0.7594 - val_accuracy: 0.5217\n",
      "Epoch 205/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3436 - accuracy: 0.8898 - val_loss: 0.7589 - val_accuracy: 0.5217\n",
      "Epoch 206/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3421 - accuracy: 0.8898 - val_loss: 0.7572 - val_accuracy: 0.5217\n",
      "Epoch 207/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3411 - accuracy: 0.8898 - val_loss: 0.7548 - val_accuracy: 0.5217\n",
      "Epoch 208/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3408 - accuracy: 0.8898 - val_loss: 0.7520 - val_accuracy: 0.5217\n",
      "Epoch 209/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3394 - accuracy: 0.8898 - val_loss: 0.7508 - val_accuracy: 0.5217\n",
      "Epoch 210/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3384 - accuracy: 0.8898 - val_loss: 0.7478 - val_accuracy: 0.5217\n",
      "Epoch 211/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3378 - accuracy: 0.8898 - val_loss: 0.7442 - val_accuracy: 0.5217\n",
      "Epoch 212/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3366 - accuracy: 0.8976 - val_loss: 0.7436 - val_accuracy: 0.5217\n",
      "Epoch 213/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3354 - accuracy: 0.8976 - val_loss: 0.7412 - val_accuracy: 0.5217\n",
      "Epoch 214/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3346 - accuracy: 0.8976 - val_loss: 0.7400 - val_accuracy: 0.5217\n",
      "Epoch 215/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3336 - accuracy: 0.8976 - val_loss: 0.7386 - val_accuracy: 0.5217\n",
      "Epoch 216/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3330 - accuracy: 0.8976 - val_loss: 0.7363 - val_accuracy: 0.5217\n",
      "Epoch 217/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3316 - accuracy: 0.8976 - val_loss: 0.7347 - val_accuracy: 0.5217\n",
      "Epoch 218/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3312 - accuracy: 0.8976 - val_loss: 0.7320 - val_accuracy: 0.5217\n",
      "Epoch 219/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3298 - accuracy: 0.8976 - val_loss: 0.7296 - val_accuracy: 0.5217\n",
      "Epoch 220/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3292 - accuracy: 0.8976 - val_loss: 0.7274 - val_accuracy: 0.5217\n",
      "Epoch 221/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3279 - accuracy: 0.9055 - val_loss: 0.7253 - val_accuracy: 0.5217\n",
      "Epoch 222/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3269 - accuracy: 0.9134 - val_loss: 0.7240 - val_accuracy: 0.5652\n",
      "Epoch 223/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3265 - accuracy: 0.9134 - val_loss: 0.7225 - val_accuracy: 0.5652\n",
      "Epoch 224/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3255 - accuracy: 0.9134 - val_loss: 0.7208 - val_accuracy: 0.5652\n",
      "Epoch 225/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.3241 - accuracy: 0.9134 - val_loss: 0.7189 - val_accuracy: 0.5652\n",
      "Epoch 226/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3232 - accuracy: 0.9134 - val_loss: 0.7166 - val_accuracy: 0.5652\n",
      "Epoch 227/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3226 - accuracy: 0.9134 - val_loss: 0.7138 - val_accuracy: 0.5652\n",
      "Epoch 228/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3217 - accuracy: 0.9134 - val_loss: 0.7115 - val_accuracy: 0.6087\n",
      "Epoch 229/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3204 - accuracy: 0.9213 - val_loss: 0.7094 - val_accuracy: 0.6522\n",
      "Epoch 230/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3196 - accuracy: 0.9213 - val_loss: 0.7083 - val_accuracy: 0.6522\n",
      "Epoch 231/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3186 - accuracy: 0.9213 - val_loss: 0.7058 - val_accuracy: 0.6522\n",
      "Epoch 232/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3177 - accuracy: 0.9213 - val_loss: 0.7045 - val_accuracy: 0.6522\n",
      "Epoch 233/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3168 - accuracy: 0.9213 - val_loss: 0.7030 - val_accuracy: 0.6522\n",
      "Epoch 234/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3159 - accuracy: 0.9213 - val_loss: 0.7004 - val_accuracy: 0.6522\n",
      "Epoch 235/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3154 - accuracy: 0.9213 - val_loss: 0.6979 - val_accuracy: 0.6522\n",
      "Epoch 236/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3139 - accuracy: 0.9213 - val_loss: 0.6961 - val_accuracy: 0.6522\n",
      "Epoch 237/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3133 - accuracy: 0.9213 - val_loss: 0.6935 - val_accuracy: 0.6522\n",
      "Epoch 238/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3120 - accuracy: 0.9213 - val_loss: 0.6915 - val_accuracy: 0.6522\n",
      "Epoch 239/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3120 - accuracy: 0.9213 - val_loss: 0.6900 - val_accuracy: 0.6522\n",
      "Epoch 240/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3110 - accuracy: 0.9291 - val_loss: 0.6887 - val_accuracy: 0.6522\n",
      "Epoch 241/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3093 - accuracy: 0.9291 - val_loss: 0.6871 - val_accuracy: 0.6522\n",
      "Epoch 242/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3087 - accuracy: 0.9370 - val_loss: 0.6866 - val_accuracy: 0.6522\n",
      "Epoch 243/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3083 - accuracy: 0.9291 - val_loss: 0.6848 - val_accuracy: 0.6522\n",
      "Epoch 244/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3067 - accuracy: 0.9370 - val_loss: 0.6830 - val_accuracy: 0.6522\n",
      "Epoch 245/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3059 - accuracy: 0.9370 - val_loss: 0.6822 - val_accuracy: 0.6522\n",
      "Epoch 246/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3052 - accuracy: 0.9370 - val_loss: 0.6808 - val_accuracy: 0.6522\n",
      "Epoch 247/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3043 - accuracy: 0.9291 - val_loss: 0.6787 - val_accuracy: 0.6522\n",
      "Epoch 248/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3032 - accuracy: 0.9370 - val_loss: 0.6767 - val_accuracy: 0.6522\n",
      "Epoch 249/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3021 - accuracy: 0.9370 - val_loss: 0.6741 - val_accuracy: 0.6522\n",
      "Epoch 250/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3017 - accuracy: 0.9370 - val_loss: 0.6712 - val_accuracy: 0.6522\n",
      "Epoch 251/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3007 - accuracy: 0.9370 - val_loss: 0.6684 - val_accuracy: 0.6522\n",
      "Epoch 252/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.2999 - accuracy: 0.9370 - val_loss: 0.6669 - val_accuracy: 0.6957\n",
      "Epoch 253/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2987 - accuracy: 0.9370 - val_loss: 0.6658 - val_accuracy: 0.6957\n",
      "Epoch 254/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2978 - accuracy: 0.9370 - val_loss: 0.6633 - val_accuracy: 0.6957\n",
      "Epoch 255/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2973 - accuracy: 0.9370 - val_loss: 0.6616 - val_accuracy: 0.6957\n",
      "Epoch 256/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2965 - accuracy: 0.9370 - val_loss: 0.6596 - val_accuracy: 0.6957\n",
      "Epoch 257/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2953 - accuracy: 0.9370 - val_loss: 0.6576 - val_accuracy: 0.6957\n",
      "Epoch 258/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2944 - accuracy: 0.9370 - val_loss: 0.6565 - val_accuracy: 0.6957\n",
      "Epoch 259/500\n",
      "127/127 [==============================] - 0s 24us/step - loss: 0.2940 - accuracy: 0.9370 - val_loss: 0.6536 - val_accuracy: 0.6957\n",
      "Epoch 260/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2929 - accuracy: 0.9370 - val_loss: 0.6505 - val_accuracy: 0.6957\n",
      "Epoch 261/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2920 - accuracy: 0.9370 - val_loss: 0.6479 - val_accuracy: 0.6957\n",
      "Epoch 262/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2907 - accuracy: 0.9370 - val_loss: 0.6462 - val_accuracy: 0.6957\n",
      "Epoch 263/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.2900 - accuracy: 0.9370 - val_loss: 0.6439 - val_accuracy: 0.6957\n",
      "Epoch 264/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2892 - accuracy: 0.9449 - val_loss: 0.6412 - val_accuracy: 0.6957\n",
      "Epoch 265/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.2885 - accuracy: 0.9449 - val_loss: 0.6410 - val_accuracy: 0.6957\n",
      "Epoch 266/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2878 - accuracy: 0.9528 - val_loss: 0.6402 - val_accuracy: 0.6957\n",
      "Epoch 267/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2869 - accuracy: 0.9370 - val_loss: 0.6369 - val_accuracy: 0.7391\n",
      "Epoch 268/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2856 - accuracy: 0.9449 - val_loss: 0.6349 - val_accuracy: 0.7391\n",
      "Epoch 269/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2850 - accuracy: 0.9449 - val_loss: 0.6320 - val_accuracy: 0.7391\n",
      "Epoch 270/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2839 - accuracy: 0.9528 - val_loss: 0.6312 - val_accuracy: 0.7391\n",
      "Epoch 271/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2831 - accuracy: 0.9449 - val_loss: 0.6288 - val_accuracy: 0.7391\n",
      "Epoch 272/500\n",
      "127/127 [==============================] - 0s 86us/step - loss: 0.2821 - accuracy: 0.9528 - val_loss: 0.6273 - val_accuracy: 0.7391\n",
      "Epoch 273/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.2821 - accuracy: 0.9449 - val_loss: 0.6246 - val_accuracy: 0.7391\n",
      "Epoch 274/500\n",
      "127/127 [==============================] - 0s 94us/step - loss: 0.2806 - accuracy: 0.9528 - val_loss: 0.6223 - val_accuracy: 0.7391\n",
      "Epoch 275/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2799 - accuracy: 0.9606 - val_loss: 0.6220 - val_accuracy: 0.7391\n",
      "Epoch 276/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.2788 - accuracy: 0.9528 - val_loss: 0.6197 - val_accuracy: 0.7391\n",
      "Epoch 277/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2780 - accuracy: 0.9606 - val_loss: 0.6181 - val_accuracy: 0.7391\n",
      "Epoch 278/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.2775 - accuracy: 0.9528 - val_loss: 0.6164 - val_accuracy: 0.7391\n",
      "Epoch 279/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2763 - accuracy: 0.9606 - val_loss: 0.6146 - val_accuracy: 0.7391\n",
      "Epoch 280/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2754 - accuracy: 0.9606 - val_loss: 0.6128 - val_accuracy: 0.7391\n",
      "Epoch 281/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.2747 - accuracy: 0.9606 - val_loss: 0.6112 - val_accuracy: 0.7391\n",
      "Epoch 282/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2738 - accuracy: 0.9606 - val_loss: 0.6095 - val_accuracy: 0.7391\n",
      "Epoch 283/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2732 - accuracy: 0.9606 - val_loss: 0.6071 - val_accuracy: 0.7391\n",
      "Epoch 284/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2723 - accuracy: 0.9606 - val_loss: 0.6050 - val_accuracy: 0.7391\n",
      "Epoch 285/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2716 - accuracy: 0.9606 - val_loss: 0.6030 - val_accuracy: 0.7391\n",
      "Epoch 286/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2704 - accuracy: 0.9606 - val_loss: 0.6015 - val_accuracy: 0.7391\n",
      "Epoch 287/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2697 - accuracy: 0.9606 - val_loss: 0.5995 - val_accuracy: 0.7391\n",
      "Epoch 288/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2688 - accuracy: 0.9606 - val_loss: 0.5982 - val_accuracy: 0.7391\n",
      "Epoch 289/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2682 - accuracy: 0.9606 - val_loss: 0.5954 - val_accuracy: 0.7391\n",
      "Epoch 290/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2673 - accuracy: 0.9606 - val_loss: 0.5937 - val_accuracy: 0.7391\n",
      "Epoch 291/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2665 - accuracy: 0.9528 - val_loss: 0.5923 - val_accuracy: 0.7391\n",
      "Epoch 292/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2657 - accuracy: 0.9606 - val_loss: 0.5910 - val_accuracy: 0.7391\n",
      "Epoch 293/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2647 - accuracy: 0.9606 - val_loss: 0.5887 - val_accuracy: 0.7391\n",
      "Epoch 294/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2640 - accuracy: 0.9606 - val_loss: 0.5868 - val_accuracy: 0.7391\n",
      "Epoch 295/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2630 - accuracy: 0.9528 - val_loss: 0.5854 - val_accuracy: 0.7391\n",
      "Epoch 296/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2621 - accuracy: 0.9606 - val_loss: 0.5832 - val_accuracy: 0.7391\n",
      "Epoch 297/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2616 - accuracy: 0.9528 - val_loss: 0.5814 - val_accuracy: 0.7391\n",
      "Epoch 298/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2607 - accuracy: 0.9528 - val_loss: 0.5798 - val_accuracy: 0.7391\n",
      "Epoch 299/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2600 - accuracy: 0.9528 - val_loss: 0.5781 - val_accuracy: 0.7391\n",
      "Epoch 300/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2591 - accuracy: 0.9606 - val_loss: 0.5767 - val_accuracy: 0.7826\n",
      "Epoch 301/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.2584 - accuracy: 0.9606 - val_loss: 0.5756 - val_accuracy: 0.7826\n",
      "Epoch 302/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.2576 - accuracy: 0.9606 - val_loss: 0.5734 - val_accuracy: 0.7826\n",
      "Epoch 303/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2567 - accuracy: 0.9606 - val_loss: 0.5710 - val_accuracy: 0.7826\n",
      "Epoch 304/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.2556 - accuracy: 0.9606 - val_loss: 0.5692 - val_accuracy: 0.7826\n",
      "Epoch 305/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2554 - accuracy: 0.9606 - val_loss: 0.5683 - val_accuracy: 0.7826\n",
      "Epoch 306/500\n",
      "127/127 [==============================] - 0s 79us/step - loss: 0.2540 - accuracy: 0.9606 - val_loss: 0.5667 - val_accuracy: 0.7826\n",
      "Epoch 307/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2535 - accuracy: 0.9606 - val_loss: 0.5650 - val_accuracy: 0.7826\n",
      "Epoch 308/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.2527 - accuracy: 0.9606 - val_loss: 0.5621 - val_accuracy: 0.7826\n",
      "Epoch 309/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2518 - accuracy: 0.9606 - val_loss: 0.5601 - val_accuracy: 0.7826\n",
      "Epoch 310/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.2513 - accuracy: 0.9606 - val_loss: 0.5590 - val_accuracy: 0.7826\n",
      "Epoch 311/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2506 - accuracy: 0.9685 - val_loss: 0.5563 - val_accuracy: 0.7826\n",
      "Epoch 312/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2493 - accuracy: 0.9606 - val_loss: 0.5540 - val_accuracy: 0.7826\n",
      "Epoch 313/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2485 - accuracy: 0.9606 - val_loss: 0.5526 - val_accuracy: 0.7826\n",
      "Epoch 314/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.2477 - accuracy: 0.9606 - val_loss: 0.5516 - val_accuracy: 0.7826\n",
      "Epoch 315/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2470 - accuracy: 0.9606 - val_loss: 0.5492 - val_accuracy: 0.7826\n",
      "Epoch 316/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2464 - accuracy: 0.9606 - val_loss: 0.5479 - val_accuracy: 0.7826\n",
      "Epoch 317/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2454 - accuracy: 0.9606 - val_loss: 0.5462 - val_accuracy: 0.7826\n",
      "Epoch 318/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2448 - accuracy: 0.9606 - val_loss: 0.5444 - val_accuracy: 0.7826\n",
      "Epoch 319/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2438 - accuracy: 0.9606 - val_loss: 0.5427 - val_accuracy: 0.7826\n",
      "Epoch 320/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2431 - accuracy: 0.9606 - val_loss: 0.5412 - val_accuracy: 0.7826\n",
      "Epoch 321/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2424 - accuracy: 0.9606 - val_loss: 0.5392 - val_accuracy: 0.7826\n",
      "Epoch 322/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2416 - accuracy: 0.9606 - val_loss: 0.5368 - val_accuracy: 0.7826\n",
      "Epoch 323/500\n",
      "127/127 [==============================] - 0s 24us/step - loss: 0.2408 - accuracy: 0.9606 - val_loss: 0.5347 - val_accuracy: 0.7826\n",
      "Epoch 324/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2404 - accuracy: 0.9606 - val_loss: 0.5319 - val_accuracy: 0.7826\n",
      "Epoch 325/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2394 - accuracy: 0.9606 - val_loss: 0.5300 - val_accuracy: 0.7826\n",
      "Epoch 326/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2385 - accuracy: 0.9606 - val_loss: 0.5293 - val_accuracy: 0.7826\n",
      "Epoch 327/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2376 - accuracy: 0.9606 - val_loss: 0.5280 - val_accuracy: 0.7826\n",
      "Epoch 328/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2370 - accuracy: 0.9606 - val_loss: 0.5266 - val_accuracy: 0.7826\n",
      "Epoch 329/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2362 - accuracy: 0.9606 - val_loss: 0.5248 - val_accuracy: 0.7826\n",
      "Epoch 330/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2359 - accuracy: 0.9606 - val_loss: 0.5227 - val_accuracy: 0.7826\n",
      "Epoch 331/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2346 - accuracy: 0.9606 - val_loss: 0.5207 - val_accuracy: 0.7826\n",
      "Epoch 332/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2339 - accuracy: 0.9606 - val_loss: 0.5197 - val_accuracy: 0.7826\n",
      "Epoch 333/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2330 - accuracy: 0.9606 - val_loss: 0.5186 - val_accuracy: 0.7826\n",
      "Epoch 334/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2323 - accuracy: 0.9606 - val_loss: 0.5172 - val_accuracy: 0.7826\n",
      "Epoch 335/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2317 - accuracy: 0.9606 - val_loss: 0.5159 - val_accuracy: 0.7826\n",
      "Epoch 336/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2310 - accuracy: 0.9606 - val_loss: 0.5152 - val_accuracy: 0.7826\n",
      "Epoch 337/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2308 - accuracy: 0.9606 - val_loss: 0.5121 - val_accuracy: 0.7826\n",
      "Epoch 338/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2293 - accuracy: 0.9606 - val_loss: 0.5107 - val_accuracy: 0.7826\n",
      "Epoch 339/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2286 - accuracy: 0.9606 - val_loss: 0.5088 - val_accuracy: 0.7826\n",
      "Epoch 340/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2278 - accuracy: 0.9606 - val_loss: 0.5074 - val_accuracy: 0.7826\n",
      "Epoch 341/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2272 - accuracy: 0.9606 - val_loss: 0.5059 - val_accuracy: 0.7826\n",
      "Epoch 342/500\n",
      "127/127 [==============================] - 0s 24us/step - loss: 0.2267 - accuracy: 0.9606 - val_loss: 0.5034 - val_accuracy: 0.7826\n",
      "Epoch 343/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.2263 - accuracy: 0.9606 - val_loss: 0.5019 - val_accuracy: 0.7826\n",
      "Epoch 344/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2249 - accuracy: 0.9685 - val_loss: 0.5007 - val_accuracy: 0.7826\n",
      "Epoch 345/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2241 - accuracy: 0.9606 - val_loss: 0.4992 - val_accuracy: 0.7826\n",
      "Epoch 346/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.2237 - accuracy: 0.9685 - val_loss: 0.4983 - val_accuracy: 0.7826\n",
      "Epoch 347/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2230 - accuracy: 0.9764 - val_loss: 0.4970 - val_accuracy: 0.7826\n",
      "Epoch 348/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.2220 - accuracy: 0.9764 - val_loss: 0.4957 - val_accuracy: 0.7826\n",
      "Epoch 349/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2214 - accuracy: 0.9764 - val_loss: 0.4950 - val_accuracy: 0.7826\n",
      "Epoch 350/500\n",
      "127/127 [==============================] - 0s 102us/step - loss: 0.2207 - accuracy: 0.9685 - val_loss: 0.4927 - val_accuracy: 0.7826\n",
      "Epoch 351/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2198 - accuracy: 0.9764 - val_loss: 0.4911 - val_accuracy: 0.7826\n",
      "Epoch 352/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.2193 - accuracy: 0.9685 - val_loss: 0.4887 - val_accuracy: 0.7826\n",
      "Epoch 353/500\n",
      "127/127 [==============================] - 0s 79us/step - loss: 0.2185 - accuracy: 0.9843 - val_loss: 0.4881 - val_accuracy: 0.7826\n",
      "Epoch 354/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2178 - accuracy: 0.9685 - val_loss: 0.4859 - val_accuracy: 0.7826\n",
      "Epoch 355/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2170 - accuracy: 0.9764 - val_loss: 0.4837 - val_accuracy: 0.7826\n",
      "Epoch 356/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2163 - accuracy: 0.9764 - val_loss: 0.4821 - val_accuracy: 0.7826\n",
      "Epoch 357/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2156 - accuracy: 0.9764 - val_loss: 0.4802 - val_accuracy: 0.7826\n",
      "Epoch 358/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2149 - accuracy: 0.9764 - val_loss: 0.4788 - val_accuracy: 0.7826\n",
      "Epoch 359/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2142 - accuracy: 0.9764 - val_loss: 0.4779 - val_accuracy: 0.7826\n",
      "Epoch 360/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2137 - accuracy: 0.9764 - val_loss: 0.4760 - val_accuracy: 0.7826\n",
      "Epoch 361/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.2131 - accuracy: 0.9764 - val_loss: 0.4747 - val_accuracy: 0.7826\n",
      "Epoch 362/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2121 - accuracy: 0.9843 - val_loss: 0.4733 - val_accuracy: 0.7826\n",
      "Epoch 363/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2113 - accuracy: 0.9843 - val_loss: 0.4722 - val_accuracy: 0.7826\n",
      "Epoch 364/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2108 - accuracy: 0.9843 - val_loss: 0.4705 - val_accuracy: 0.7826\n",
      "Epoch 365/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2103 - accuracy: 0.9764 - val_loss: 0.4684 - val_accuracy: 0.7826\n",
      "Epoch 366/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.2093 - accuracy: 0.9843 - val_loss: 0.4670 - val_accuracy: 0.7826\n",
      "Epoch 367/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2086 - accuracy: 0.9843 - val_loss: 0.4656 - val_accuracy: 0.7826\n",
      "Epoch 368/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2081 - accuracy: 0.9843 - val_loss: 0.4651 - val_accuracy: 0.7826\n",
      "Epoch 369/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2079 - accuracy: 0.9685 - val_loss: 0.4625 - val_accuracy: 0.7826\n",
      "Epoch 370/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2065 - accuracy: 0.9843 - val_loss: 0.4612 - val_accuracy: 0.7826\n",
      "Epoch 371/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.2059 - accuracy: 0.9843 - val_loss: 0.4600 - val_accuracy: 0.7826\n",
      "Epoch 372/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2053 - accuracy: 0.9843 - val_loss: 0.4591 - val_accuracy: 0.7826\n",
      "Epoch 373/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.2045 - accuracy: 0.9843 - val_loss: 0.4581 - val_accuracy: 0.7826\n",
      "Epoch 374/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2039 - accuracy: 0.9843 - val_loss: 0.4568 - val_accuracy: 0.7826\n",
      "Epoch 375/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2033 - accuracy: 0.9843 - val_loss: 0.4553 - val_accuracy: 0.7826\n",
      "Epoch 376/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.2025 - accuracy: 0.9843 - val_loss: 0.4536 - val_accuracy: 0.7826\n",
      "Epoch 377/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2019 - accuracy: 0.9843 - val_loss: 0.4528 - val_accuracy: 0.7826\n",
      "Epoch 378/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2013 - accuracy: 0.9843 - val_loss: 0.4519 - val_accuracy: 0.7826\n",
      "Epoch 379/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2006 - accuracy: 0.9843 - val_loss: 0.4507 - val_accuracy: 0.7826\n",
      "Epoch 380/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1998 - accuracy: 0.9843 - val_loss: 0.4495 - val_accuracy: 0.7826\n",
      "Epoch 381/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1992 - accuracy: 0.9843 - val_loss: 0.4481 - val_accuracy: 0.7826\n",
      "Epoch 382/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1989 - accuracy: 0.9843 - val_loss: 0.4479 - val_accuracy: 0.7826\n",
      "Epoch 383/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1980 - accuracy: 0.9843 - val_loss: 0.4467 - val_accuracy: 0.7826\n",
      "Epoch 384/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.1974 - accuracy: 0.9843 - val_loss: 0.4450 - val_accuracy: 0.7826\n",
      "Epoch 385/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1967 - accuracy: 0.9843 - val_loss: 0.4433 - val_accuracy: 0.7826\n",
      "Epoch 386/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1960 - accuracy: 0.9843 - val_loss: 0.4423 - val_accuracy: 0.7826\n",
      "Epoch 387/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.1956 - accuracy: 0.9843 - val_loss: 0.4407 - val_accuracy: 0.7826\n",
      "Epoch 388/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1948 - accuracy: 0.9843 - val_loss: 0.4390 - val_accuracy: 0.7826\n",
      "Epoch 389/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.1942 - accuracy: 0.9843 - val_loss: 0.4369 - val_accuracy: 0.7826\n",
      "Epoch 390/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1935 - accuracy: 0.9843 - val_loss: 0.4362 - val_accuracy: 0.7826\n",
      "Epoch 391/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1929 - accuracy: 0.9843 - val_loss: 0.4348 - val_accuracy: 0.7826\n",
      "Epoch 392/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1924 - accuracy: 0.9843 - val_loss: 0.4331 - val_accuracy: 0.7826\n",
      "Epoch 393/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1916 - accuracy: 0.9843 - val_loss: 0.4322 - val_accuracy: 0.7826\n",
      "Epoch 394/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.1910 - accuracy: 0.9843 - val_loss: 0.4308 - val_accuracy: 0.7826\n",
      "Epoch 395/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1904 - accuracy: 0.9843 - val_loss: 0.4300 - val_accuracy: 0.7826\n",
      "Epoch 396/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.1899 - accuracy: 0.9843 - val_loss: 0.4281 - val_accuracy: 0.7826\n",
      "Epoch 397/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.1897 - accuracy: 0.9843 - val_loss: 0.4269 - val_accuracy: 0.7826\n",
      "Epoch 398/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.1886 - accuracy: 0.9843 - val_loss: 0.4251 - val_accuracy: 0.8261\n",
      "Epoch 399/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1881 - accuracy: 0.9843 - val_loss: 0.4236 - val_accuracy: 0.8261\n",
      "Epoch 400/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.1876 - accuracy: 0.9843 - val_loss: 0.4216 - val_accuracy: 0.8261\n",
      "Epoch 401/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1867 - accuracy: 0.9843 - val_loss: 0.4207 - val_accuracy: 0.8261\n",
      "Epoch 402/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1862 - accuracy: 0.9843 - val_loss: 0.4193 - val_accuracy: 0.8261\n",
      "Epoch 403/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.1857 - accuracy: 0.9843 - val_loss: 0.4182 - val_accuracy: 0.8261\n",
      "Epoch 404/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.1854 - accuracy: 0.9843 - val_loss: 0.4169 - val_accuracy: 0.8261\n",
      "Epoch 405/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.1843 - accuracy: 0.9843 - val_loss: 0.4163 - val_accuracy: 0.8261\n",
      "Epoch 406/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.1837 - accuracy: 0.9843 - val_loss: 0.4148 - val_accuracy: 0.8261\n",
      "Epoch 407/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1834 - accuracy: 0.9843 - val_loss: 0.4130 - val_accuracy: 0.8261\n",
      "Epoch 408/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.1825 - accuracy: 0.9843 - val_loss: 0.4122 - val_accuracy: 0.8261\n",
      "Epoch 409/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.1819 - accuracy: 0.9843 - val_loss: 0.4110 - val_accuracy: 0.8261\n",
      "Epoch 410/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1813 - accuracy: 0.9843 - val_loss: 0.4102 - val_accuracy: 0.8261\n",
      "Epoch 411/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1810 - accuracy: 0.9843 - val_loss: 0.4095 - val_accuracy: 0.8261\n",
      "Epoch 412/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1802 - accuracy: 0.9843 - val_loss: 0.4081 - val_accuracy: 0.8261\n",
      "Epoch 413/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1797 - accuracy: 0.9843 - val_loss: 0.4066 - val_accuracy: 0.8261\n",
      "Epoch 414/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1790 - accuracy: 0.9843 - val_loss: 0.4054 - val_accuracy: 0.8261\n",
      "Epoch 415/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1786 - accuracy: 0.9843 - val_loss: 0.4048 - val_accuracy: 0.8261\n",
      "Epoch 416/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1779 - accuracy: 0.9843 - val_loss: 0.4040 - val_accuracy: 0.8261\n",
      "Epoch 417/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1773 - accuracy: 0.9843 - val_loss: 0.4029 - val_accuracy: 0.8261\n",
      "Epoch 418/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1767 - accuracy: 0.9843 - val_loss: 0.4015 - val_accuracy: 0.8261\n",
      "Epoch 419/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1763 - accuracy: 0.9843 - val_loss: 0.4005 - val_accuracy: 0.8261\n",
      "Epoch 420/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1758 - accuracy: 0.9843 - val_loss: 0.3996 - val_accuracy: 0.8261\n",
      "Epoch 421/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1754 - accuracy: 0.9843 - val_loss: 0.3975 - val_accuracy: 0.8261\n",
      "Epoch 422/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1745 - accuracy: 0.9843 - val_loss: 0.3962 - val_accuracy: 0.8261\n",
      "Epoch 423/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1740 - accuracy: 0.9843 - val_loss: 0.3951 - val_accuracy: 0.8261\n",
      "Epoch 424/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1734 - accuracy: 0.9843 - val_loss: 0.3941 - val_accuracy: 0.8261\n",
      "Epoch 425/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1730 - accuracy: 0.9843 - val_loss: 0.3932 - val_accuracy: 0.8261\n",
      "Epoch 426/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1722 - accuracy: 0.9843 - val_loss: 0.3922 - val_accuracy: 0.8261\n",
      "Epoch 427/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1718 - accuracy: 0.9843 - val_loss: 0.3917 - val_accuracy: 0.8261\n",
      "Epoch 428/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1712 - accuracy: 0.9843 - val_loss: 0.3908 - val_accuracy: 0.8261\n",
      "Epoch 429/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1706 - accuracy: 0.9843 - val_loss: 0.3901 - val_accuracy: 0.8261\n",
      "Epoch 430/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1705 - accuracy: 0.9843 - val_loss: 0.3893 - val_accuracy: 0.8261\n",
      "Epoch 431/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1696 - accuracy: 0.9843 - val_loss: 0.3878 - val_accuracy: 0.8261\n",
      "Epoch 432/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1691 - accuracy: 0.9843 - val_loss: 0.3869 - val_accuracy: 0.8261\n",
      "Epoch 433/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1686 - accuracy: 0.9843 - val_loss: 0.3859 - val_accuracy: 0.8261\n",
      "Epoch 434/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 0.1679 - accuracy: 0.9843 - val_loss: 0.3851 - val_accuracy: 0.8261\n",
      "Epoch 435/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1675 - accuracy: 0.9843 - val_loss: 0.3834 - val_accuracy: 0.8261\n",
      "Epoch 436/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1669 - accuracy: 0.9843 - val_loss: 0.3825 - val_accuracy: 0.8261\n",
      "Epoch 437/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1665 - accuracy: 0.9843 - val_loss: 0.3819 - val_accuracy: 0.8261\n",
      "Epoch 438/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1659 - accuracy: 0.9843 - val_loss: 0.3809 - val_accuracy: 0.8261\n",
      "Epoch 439/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1654 - accuracy: 0.9843 - val_loss: 0.3797 - val_accuracy: 0.8261\n",
      "Epoch 440/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1648 - accuracy: 0.9843 - val_loss: 0.3787 - val_accuracy: 0.8261\n",
      "Epoch 441/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1644 - accuracy: 0.9843 - val_loss: 0.3776 - val_accuracy: 0.8261\n",
      "Epoch 442/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1638 - accuracy: 0.9843 - val_loss: 0.3763 - val_accuracy: 0.8261\n",
      "Epoch 443/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1633 - accuracy: 0.9843 - val_loss: 0.3750 - val_accuracy: 0.8261\n",
      "Epoch 444/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1627 - accuracy: 0.9843 - val_loss: 0.3742 - val_accuracy: 0.8261\n",
      "Epoch 445/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1623 - accuracy: 0.9843 - val_loss: 0.3729 - val_accuracy: 0.8261\n",
      "Epoch 446/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1619 - accuracy: 0.9843 - val_loss: 0.3719 - val_accuracy: 0.8261\n",
      "Epoch 447/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1612 - accuracy: 0.9843 - val_loss: 0.3710 - val_accuracy: 0.8696\n",
      "Epoch 448/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1609 - accuracy: 0.9843 - val_loss: 0.3699 - val_accuracy: 0.8696\n",
      "Epoch 449/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1603 - accuracy: 0.9843 - val_loss: 0.3695 - val_accuracy: 0.8696\n",
      "Epoch 450/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1598 - accuracy: 0.9843 - val_loss: 0.3682 - val_accuracy: 0.8696\n",
      "Epoch 451/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1593 - accuracy: 0.9843 - val_loss: 0.3670 - val_accuracy: 0.8696\n",
      "Epoch 452/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1591 - accuracy: 0.9843 - val_loss: 0.3661 - val_accuracy: 0.8696\n",
      "Epoch 453/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1584 - accuracy: 0.9843 - val_loss: 0.3645 - val_accuracy: 0.8696\n",
      "Epoch 454/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1580 - accuracy: 0.9843 - val_loss: 0.3641 - val_accuracy: 0.8696\n",
      "Epoch 455/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1574 - accuracy: 0.9843 - val_loss: 0.3633 - val_accuracy: 0.8696\n",
      "Epoch 456/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1570 - accuracy: 0.9843 - val_loss: 0.3619 - val_accuracy: 0.8696\n",
      "Epoch 457/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1563 - accuracy: 0.9843 - val_loss: 0.3612 - val_accuracy: 0.8696\n",
      "Epoch 458/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1559 - accuracy: 0.9843 - val_loss: 0.3606 - val_accuracy: 0.8696\n",
      "Epoch 459/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1558 - accuracy: 0.9843 - val_loss: 0.3588 - val_accuracy: 0.8696\n",
      "Epoch 460/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1550 - accuracy: 0.9843 - val_loss: 0.3576 - val_accuracy: 0.8696\n",
      "Epoch 461/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1545 - accuracy: 0.9843 - val_loss: 0.3566 - val_accuracy: 0.8696\n",
      "Epoch 462/500\n",
      "127/127 [==============================] - 0s 24us/step - loss: 0.1541 - accuracy: 0.9843 - val_loss: 0.3564 - val_accuracy: 0.8696\n",
      "Epoch 463/500\n",
      "127/127 [==============================] - 0s 24us/step - loss: 0.1536 - accuracy: 0.9843 - val_loss: 0.3555 - val_accuracy: 0.8696\n",
      "Epoch 464/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1531 - accuracy: 0.9843 - val_loss: 0.3554 - val_accuracy: 0.8696\n",
      "Epoch 465/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1527 - accuracy: 0.9843 - val_loss: 0.3540 - val_accuracy: 0.8696\n",
      "Epoch 466/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1525 - accuracy: 0.9764 - val_loss: 0.3542 - val_accuracy: 0.8696\n",
      "Epoch 467/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1520 - accuracy: 0.9843 - val_loss: 0.3541 - val_accuracy: 0.8696\n",
      "Epoch 468/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1515 - accuracy: 0.9843 - val_loss: 0.3533 - val_accuracy: 0.8696\n",
      "Epoch 469/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1508 - accuracy: 0.9843 - val_loss: 0.3526 - val_accuracy: 0.8696\n",
      "Epoch 470/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1505 - accuracy: 0.9843 - val_loss: 0.3518 - val_accuracy: 0.8696\n",
      "Epoch 471/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1498 - accuracy: 0.9843 - val_loss: 0.3509 - val_accuracy: 0.8696\n",
      "Epoch 472/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1496 - accuracy: 0.9843 - val_loss: 0.3502 - val_accuracy: 0.8696\n",
      "Epoch 473/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1490 - accuracy: 0.9843 - val_loss: 0.3487 - val_accuracy: 0.8696\n",
      "Epoch 474/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1487 - accuracy: 0.9843 - val_loss: 0.3484 - val_accuracy: 0.8696\n",
      "Epoch 475/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1482 - accuracy: 0.9843 - val_loss: 0.3474 - val_accuracy: 0.8696\n",
      "Epoch 476/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1477 - accuracy: 0.9843 - val_loss: 0.3468 - val_accuracy: 0.8696\n",
      "Epoch 477/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1476 - accuracy: 0.9843 - val_loss: 0.3451 - val_accuracy: 0.8696\n",
      "Epoch 478/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1468 - accuracy: 0.9843 - val_loss: 0.3445 - val_accuracy: 0.8696\n",
      "Epoch 479/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1463 - accuracy: 0.9843 - val_loss: 0.3440 - val_accuracy: 0.8696\n",
      "Epoch 480/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1460 - accuracy: 0.9843 - val_loss: 0.3431 - val_accuracy: 0.8696\n",
      "Epoch 481/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1455 - accuracy: 0.9764 - val_loss: 0.3424 - val_accuracy: 0.8696\n",
      "Epoch 482/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1450 - accuracy: 0.9843 - val_loss: 0.3416 - val_accuracy: 0.8696\n",
      "Epoch 483/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1446 - accuracy: 0.9843 - val_loss: 0.3409 - val_accuracy: 0.8696\n",
      "Epoch 484/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1442 - accuracy: 0.9764 - val_loss: 0.3404 - val_accuracy: 0.8696\n",
      "Epoch 485/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1441 - accuracy: 0.9843 - val_loss: 0.3393 - val_accuracy: 0.8696\n",
      "Epoch 486/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1437 - accuracy: 0.9843 - val_loss: 0.3381 - val_accuracy: 0.8696\n",
      "Epoch 487/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1432 - accuracy: 0.9843 - val_loss: 0.3366 - val_accuracy: 0.8696\n",
      "Epoch 488/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.1428 - accuracy: 0.9843 - val_loss: 0.3352 - val_accuracy: 0.8696\n",
      "Epoch 489/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1422 - accuracy: 0.9764 - val_loss: 0.3340 - val_accuracy: 0.8696\n",
      "Epoch 490/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1418 - accuracy: 0.9764 - val_loss: 0.3341 - val_accuracy: 0.8696\n",
      "Epoch 491/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1413 - accuracy: 0.9764 - val_loss: 0.3340 - val_accuracy: 0.8696\n",
      "Epoch 492/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1408 - accuracy: 0.9764 - val_loss: 0.3332 - val_accuracy: 0.8696\n",
      "Epoch 493/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1406 - accuracy: 0.9764 - val_loss: 0.3324 - val_accuracy: 0.8696\n",
      "Epoch 494/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.1400 - accuracy: 0.9764 - val_loss: 0.3321 - val_accuracy: 0.8696\n",
      "Epoch 495/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1397 - accuracy: 0.9764 - val_loss: 0.3311 - val_accuracy: 0.8696\n",
      "Epoch 496/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1392 - accuracy: 0.9764 - val_loss: 0.3306 - val_accuracy: 0.8696\n",
      "Epoch 497/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1388 - accuracy: 0.9764 - val_loss: 0.3300 - val_accuracy: 0.8696\n",
      "Epoch 498/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1385 - accuracy: 0.9764 - val_loss: 0.3294 - val_accuracy: 0.8696\n",
      "Epoch 499/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.1382 - accuracy: 0.9764 - val_loss: 0.3284 - val_accuracy: 0.8696\n",
      "Epoch 500/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.1377 - accuracy: 0.9764 - val_loss: 0.3280 - val_accuracy: 0.8696\n"
     ]
    }
   ],
   "source": [
    "history01=model_ada.fit(input_data,correct_data,validation_split=0.15,epochs=500)\n",
    "ans_ada=model_ada.predict(input_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy : 94.67%\n"
     ]
    }
   ],
   "source": [
    "score=model_ada.evaluate(input_test,correct_test,verbose=0)\n",
    "print(\"Test accuracy : %.2f%%\" %(score[1]*100))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(500),history01.history['accuracy'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 127 samples, validate on 23 samples\n",
      "Epoch 1/500\n",
      "127/127 [==============================] - 0s 1ms/step - loss: 1.1862 - accuracy: 0.3307 - val_loss: 1.2034 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.1538 - accuracy: 0.3386 - val_loss: 1.2971 - val_accuracy: 0.0000e+00\n",
      "Epoch 3/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.1011 - accuracy: 0.4409 - val_loss: 1.4013 - val_accuracy: 0.0000e+00\n",
      "Epoch 4/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 1.0715 - accuracy: 0.4094 - val_loss: 1.4395 - val_accuracy: 0.0000e+00\n",
      "Epoch 5/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.1027 - accuracy: 0.4252 - val_loss: 1.4779 - val_accuracy: 0.0000e+00\n",
      "Epoch 6/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.1157 - accuracy: 0.3622 - val_loss: 1.4639 - val_accuracy: 0.0000e+00\n",
      "Epoch 7/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.1424 - accuracy: 0.3386 - val_loss: 1.4648 - val_accuracy: 0.0000e+00\n",
      "Epoch 8/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0239 - accuracy: 0.4724 - val_loss: 1.4755 - val_accuracy: 0.0000e+00\n",
      "Epoch 9/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 1.0961 - accuracy: 0.3701 - val_loss: 1.5061 - val_accuracy: 0.0000e+00\n",
      "Epoch 10/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0794 - accuracy: 0.4173 - val_loss: 1.5165 - val_accuracy: 0.0000e+00\n",
      "Epoch 11/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.1462 - accuracy: 0.3622 - val_loss: 1.5168 - val_accuracy: 0.0000e+00\n",
      "Epoch 12/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0657 - accuracy: 0.4331 - val_loss: 1.5173 - val_accuracy: 0.0000e+00\n",
      "Epoch 13/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0102 - accuracy: 0.5118 - val_loss: 1.5182 - val_accuracy: 0.0000e+00\n",
      "Epoch 14/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0766 - accuracy: 0.4094 - val_loss: 1.5172 - val_accuracy: 0.0000e+00\n",
      "Epoch 15/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0526 - accuracy: 0.4646 - val_loss: 1.5251 - val_accuracy: 0.0000e+00\n",
      "Epoch 16/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0691 - accuracy: 0.5039 - val_loss: 1.5085 - val_accuracy: 0.0000e+00\n",
      "Epoch 17/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0699 - accuracy: 0.4331 - val_loss: 1.5173 - val_accuracy: 0.0000e+00\n",
      "Epoch 18/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0270 - accuracy: 0.4724 - val_loss: 1.5074 - val_accuracy: 0.0000e+00\n",
      "Epoch 19/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0347 - accuracy: 0.4252 - val_loss: 1.5091 - val_accuracy: 0.0000e+00\n",
      "Epoch 20/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.9941 - accuracy: 0.5512 - val_loss: 1.4998 - val_accuracy: 0.0000e+00\n",
      "Epoch 21/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0331 - accuracy: 0.4409 - val_loss: 1.4915 - val_accuracy: 0.0000e+00\n",
      "Epoch 22/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0036 - accuracy: 0.5512 - val_loss: 1.4827 - val_accuracy: 0.0000e+00\n",
      "Epoch 23/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.9959 - accuracy: 0.5591 - val_loss: 1.4784 - val_accuracy: 0.0000e+00\n",
      "Epoch 24/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0102 - accuracy: 0.5276 - val_loss: 1.4677 - val_accuracy: 0.0000e+00\n",
      "Epoch 25/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.9397 - accuracy: 0.6063 - val_loss: 1.4650 - val_accuracy: 0.0000e+00\n",
      "Epoch 26/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.9722 - accuracy: 0.6063 - val_loss: 1.4514 - val_accuracy: 0.0000e+00\n",
      "Epoch 27/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.9849 - accuracy: 0.5748 - val_loss: 1.4476 - val_accuracy: 0.0000e+00\n",
      "Epoch 28/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 0.9801 - accuracy: 0.5512 - val_loss: 1.4405 - val_accuracy: 0.0000e+00\n",
      "Epoch 29/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.9554 - accuracy: 0.5906 - val_loss: 1.4283 - val_accuracy: 0.0000e+00\n",
      "Epoch 30/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.9715 - accuracy: 0.5669 - val_loss: 1.4245 - val_accuracy: 0.0000e+00\n",
      "Epoch 31/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.9708 - accuracy: 0.6063 - val_loss: 1.4104 - val_accuracy: 0.0000e+00\n",
      "Epoch 32/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.9319 - accuracy: 0.6220 - val_loss: 1.3968 - val_accuracy: 0.0000e+00\n",
      "Epoch 33/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.9053 - accuracy: 0.6614 - val_loss: 1.3901 - val_accuracy: 0.0000e+00\n",
      "Epoch 34/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.9306 - accuracy: 0.5669 - val_loss: 1.3834 - val_accuracy: 0.0000e+00\n",
      "Epoch 35/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.9119 - accuracy: 0.6850 - val_loss: 1.3687 - val_accuracy: 0.0000e+00\n",
      "Epoch 36/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.9162 - accuracy: 0.6378 - val_loss: 1.3577 - val_accuracy: 0.0000e+00\n",
      "Epoch 37/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 0.9430 - accuracy: 0.6063 - val_loss: 1.3499 - val_accuracy: 0.0000e+00\n",
      "Epoch 38/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.9123 - accuracy: 0.6299 - val_loss: 1.3367 - val_accuracy: 0.0000e+00\n",
      "Epoch 39/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.8652 - accuracy: 0.6614 - val_loss: 1.3360 - val_accuracy: 0.0000e+00\n",
      "Epoch 40/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.8570 - accuracy: 0.6850 - val_loss: 1.3224 - val_accuracy: 0.0000e+00\n",
      "Epoch 41/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 0.8797 - accuracy: 0.6378 - val_loss: 1.3151 - val_accuracy: 0.0000e+00\n",
      "Epoch 42/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.8905 - accuracy: 0.6378 - val_loss: 1.3067 - val_accuracy: 0.0000e+00\n",
      "Epoch 43/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.8597 - accuracy: 0.6535 - val_loss: 1.2970 - val_accuracy: 0.0000e+00\n",
      "Epoch 44/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.8404 - accuracy: 0.6299 - val_loss: 1.2904 - val_accuracy: 0.0000e+00\n",
      "Epoch 45/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.8370 - accuracy: 0.7087 - val_loss: 1.2806 - val_accuracy: 0.0000e+00\n",
      "Epoch 46/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.8474 - accuracy: 0.6693 - val_loss: 1.2700 - val_accuracy: 0.0000e+00\n",
      "Epoch 47/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.8299 - accuracy: 0.6535 - val_loss: 1.2676 - val_accuracy: 0.0000e+00\n",
      "Epoch 48/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.7402 - accuracy: 0.7244 - val_loss: 1.2639 - val_accuracy: 0.0000e+00\n",
      "Epoch 49/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.7525 - accuracy: 0.7165 - val_loss: 1.2548 - val_accuracy: 0.0000e+00\n",
      "Epoch 50/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.7558 - accuracy: 0.7559 - val_loss: 1.2487 - val_accuracy: 0.0000e+00\n",
      "Epoch 51/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.7728 - accuracy: 0.7244 - val_loss: 1.2401 - val_accuracy: 0.0000e+00\n",
      "Epoch 52/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.7505 - accuracy: 0.7008 - val_loss: 1.2296 - val_accuracy: 0.0000e+00\n",
      "Epoch 53/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.7840 - accuracy: 0.7244 - val_loss: 1.2243 - val_accuracy: 0.0000e+00\n",
      "Epoch 54/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.7469 - accuracy: 0.7480 - val_loss: 1.2174 - val_accuracy: 0.0000e+00\n",
      "Epoch 55/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.7222 - accuracy: 0.7402 - val_loss: 1.2104 - val_accuracy: 0.0000e+00\n",
      "Epoch 56/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.7332 - accuracy: 0.7559 - val_loss: 1.2005 - val_accuracy: 0.0000e+00\n",
      "Epoch 57/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.7167 - accuracy: 0.7402 - val_loss: 1.2006 - val_accuracy: 0.0000e+00\n",
      "Epoch 58/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.7176 - accuracy: 0.7244 - val_loss: 1.1946 - val_accuracy: 0.0000e+00\n",
      "Epoch 59/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.6885 - accuracy: 0.7402 - val_loss: 1.1845 - val_accuracy: 0.0000e+00\n",
      "Epoch 60/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.7064 - accuracy: 0.7402 - val_loss: 1.1722 - val_accuracy: 0.0000e+00\n",
      "Epoch 61/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.7303 - accuracy: 0.7087 - val_loss: 1.1675 - val_accuracy: 0.0000e+00\n",
      "Epoch 62/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.6991 - accuracy: 0.7480 - val_loss: 1.1569 - val_accuracy: 0.0000e+00\n",
      "Epoch 63/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.6721 - accuracy: 0.7402 - val_loss: 1.1507 - val_accuracy: 0.0000e+00\n",
      "Epoch 64/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.7432 - accuracy: 0.7244 - val_loss: 1.1383 - val_accuracy: 0.0000e+00\n",
      "Epoch 65/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.6791 - accuracy: 0.7323 - val_loss: 1.1324 - val_accuracy: 0.0000e+00\n",
      "Epoch 66/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.7030 - accuracy: 0.6850 - val_loss: 1.1295 - val_accuracy: 0.0000e+00\n",
      "Epoch 67/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.6450 - accuracy: 0.7795 - val_loss: 1.1312 - val_accuracy: 0.0000e+00\n",
      "Epoch 68/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.6689 - accuracy: 0.7480 - val_loss: 1.1340 - val_accuracy: 0.0000e+00\n",
      "Epoch 69/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.6643 - accuracy: 0.7323 - val_loss: 1.1243 - val_accuracy: 0.0000e+00\n",
      "Epoch 70/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.6451 - accuracy: 0.7008 - val_loss: 1.1209 - val_accuracy: 0.0000e+00\n",
      "Epoch 71/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.6728 - accuracy: 0.7402 - val_loss: 1.1151 - val_accuracy: 0.0000e+00\n",
      "Epoch 72/500\n",
      "127/127 [==============================] - 0s 70us/step - loss: 0.6336 - accuracy: 0.7795 - val_loss: 1.1097 - val_accuracy: 0.0000e+00\n",
      "Epoch 73/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.6439 - accuracy: 0.7244 - val_loss: 1.1086 - val_accuracy: 0.0000e+00\n",
      "Epoch 74/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.6003 - accuracy: 0.7795 - val_loss: 1.1099 - val_accuracy: 0.0000e+00\n",
      "Epoch 75/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.5929 - accuracy: 0.7795 - val_loss: 1.1065 - val_accuracy: 0.0000e+00\n",
      "Epoch 76/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.6005 - accuracy: 0.7480 - val_loss: 1.1023 - val_accuracy: 0.0000e+00\n",
      "Epoch 77/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.6173 - accuracy: 0.7717 - val_loss: 1.0960 - val_accuracy: 0.0000e+00\n",
      "Epoch 78/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5877 - accuracy: 0.7795 - val_loss: 1.0894 - val_accuracy: 0.0000e+00\n",
      "Epoch 79/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.6065 - accuracy: 0.7480 - val_loss: 1.0793 - val_accuracy: 0.0000e+00\n",
      "Epoch 80/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.6232 - accuracy: 0.7480 - val_loss: 1.0738 - val_accuracy: 0.0000e+00\n",
      "Epoch 81/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5942 - accuracy: 0.7795 - val_loss: 1.0720 - val_accuracy: 0.0000e+00\n",
      "Epoch 82/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.5927 - accuracy: 0.7402 - val_loss: 1.0688 - val_accuracy: 0.0000e+00\n",
      "Epoch 83/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5894 - accuracy: 0.7874 - val_loss: 1.0618 - val_accuracy: 0.0000e+00\n",
      "Epoch 84/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.5769 - accuracy: 0.7795 - val_loss: 1.0525 - val_accuracy: 0.0000e+00\n",
      "Epoch 85/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5931 - accuracy: 0.7480 - val_loss: 1.0475 - val_accuracy: 0.0000e+00\n",
      "Epoch 86/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5823 - accuracy: 0.7402 - val_loss: 1.0552 - val_accuracy: 0.0000e+00\n",
      "Epoch 87/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5742 - accuracy: 0.7795 - val_loss: 1.0569 - val_accuracy: 0.0000e+00\n",
      "Epoch 88/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5818 - accuracy: 0.7795 - val_loss: 1.0569 - val_accuracy: 0.0000e+00\n",
      "Epoch 89/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5833 - accuracy: 0.7244 - val_loss: 1.0470 - val_accuracy: 0.0000e+00\n",
      "Epoch 90/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5679 - accuracy: 0.7402 - val_loss: 1.0465 - val_accuracy: 0.0000e+00\n",
      "Epoch 91/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.5634 - accuracy: 0.7638 - val_loss: 1.0471 - val_accuracy: 0.0000e+00\n",
      "Epoch 92/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.5581 - accuracy: 0.7559 - val_loss: 1.0410 - val_accuracy: 0.0000e+00\n",
      "Epoch 93/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.5179 - accuracy: 0.7795 - val_loss: 1.0431 - val_accuracy: 0.0000e+00\n",
      "Epoch 94/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5490 - accuracy: 0.7717 - val_loss: 1.0431 - val_accuracy: 0.0000e+00\n",
      "Epoch 95/500\n",
      "127/127 [==============================] - 0s 70us/step - loss: 0.5730 - accuracy: 0.7795 - val_loss: 1.0438 - val_accuracy: 0.0000e+00\n",
      "Epoch 96/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5763 - accuracy: 0.7402 - val_loss: 1.0428 - val_accuracy: 0.0000e+00\n",
      "Epoch 97/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.5038 - accuracy: 0.7953 - val_loss: 1.0485 - val_accuracy: 0.0000e+00\n",
      "Epoch 98/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5683 - accuracy: 0.7244 - val_loss: 1.0441 - val_accuracy: 0.0000e+00\n",
      "Epoch 99/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5373 - accuracy: 0.7638 - val_loss: 1.0423 - val_accuracy: 0.0000e+00\n",
      "Epoch 100/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5262 - accuracy: 0.7244 - val_loss: 1.0420 - val_accuracy: 0.0000e+00\n",
      "Epoch 101/500\n",
      "127/127 [==============================] - 0s 79us/step - loss: 0.5265 - accuracy: 0.7480 - val_loss: 1.0386 - val_accuracy: 0.0000e+00\n",
      "Epoch 102/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5492 - accuracy: 0.7559 - val_loss: 1.0356 - val_accuracy: 0.0000e+00\n",
      "Epoch 103/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5203 - accuracy: 0.7638 - val_loss: 1.0341 - val_accuracy: 0.0000e+00\n",
      "Epoch 104/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5382 - accuracy: 0.7638 - val_loss: 1.0309 - val_accuracy: 0.0000e+00\n",
      "Epoch 105/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5510 - accuracy: 0.7244 - val_loss: 1.0311 - val_accuracy: 0.0000e+00\n",
      "Epoch 106/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.5579 - accuracy: 0.7638 - val_loss: 1.0325 - val_accuracy: 0.0000e+00\n",
      "Epoch 107/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5425 - accuracy: 0.7638 - val_loss: 1.0342 - val_accuracy: 0.0000e+00\n",
      "Epoch 108/500\n",
      "127/127 [==============================] - 0s 79us/step - loss: 0.5433 - accuracy: 0.7323 - val_loss: 1.0317 - val_accuracy: 0.0000e+00\n",
      "Epoch 109/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.5104 - accuracy: 0.7795 - val_loss: 1.0332 - val_accuracy: 0.0000e+00\n",
      "Epoch 110/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5119 - accuracy: 0.7717 - val_loss: 1.0366 - val_accuracy: 0.0000e+00\n",
      "Epoch 111/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5466 - accuracy: 0.7795 - val_loss: 1.0321 - val_accuracy: 0.0000e+00\n",
      "Epoch 112/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.5192 - accuracy: 0.7717 - val_loss: 1.0282 - val_accuracy: 0.0000e+00\n",
      "Epoch 113/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5556 - accuracy: 0.7480 - val_loss: 1.0276 - val_accuracy: 0.0000e+00\n",
      "Epoch 114/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.5175 - accuracy: 0.7480 - val_loss: 1.0224 - val_accuracy: 0.0000e+00\n",
      "Epoch 115/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5137 - accuracy: 0.7874 - val_loss: 1.0255 - val_accuracy: 0.0000e+00\n",
      "Epoch 116/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.5232 - accuracy: 0.7402 - val_loss: 1.0209 - val_accuracy: 0.0000e+00\n",
      "Epoch 117/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4812 - accuracy: 0.7795 - val_loss: 1.0197 - val_accuracy: 0.0000e+00\n",
      "Epoch 118/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4980 - accuracy: 0.7795 - val_loss: 1.0182 - val_accuracy: 0.0000e+00\n",
      "Epoch 119/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5222 - accuracy: 0.7165 - val_loss: 1.0215 - val_accuracy: 0.0000e+00\n",
      "Epoch 120/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5059 - accuracy: 0.7638 - val_loss: 1.0246 - val_accuracy: 0.0000e+00\n",
      "Epoch 121/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.5094 - accuracy: 0.7559 - val_loss: 1.0278 - val_accuracy: 0.0000e+00\n",
      "Epoch 122/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5185 - accuracy: 0.7402 - val_loss: 1.0269 - val_accuracy: 0.0000e+00\n",
      "Epoch 123/500\n",
      "127/127 [==============================] - 0s 79us/step - loss: 0.5124 - accuracy: 0.7244 - val_loss: 1.0216 - val_accuracy: 0.0000e+00\n",
      "Epoch 124/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5008 - accuracy: 0.7638 - val_loss: 1.0232 - val_accuracy: 0.0000e+00\n",
      "Epoch 125/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.5108 - accuracy: 0.7638 - val_loss: 1.0195 - val_accuracy: 0.0000e+00\n",
      "Epoch 126/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4936 - accuracy: 0.7717 - val_loss: 1.0144 - val_accuracy: 0.0000e+00\n",
      "Epoch 127/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5197 - accuracy: 0.7953 - val_loss: 1.0133 - val_accuracy: 0.0000e+00\n",
      "Epoch 128/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4759 - accuracy: 0.8031 - val_loss: 1.0123 - val_accuracy: 0.0000e+00\n",
      "Epoch 129/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4815 - accuracy: 0.7953 - val_loss: 1.0138 - val_accuracy: 0.0000e+00\n",
      "Epoch 130/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.5278 - accuracy: 0.7402 - val_loss: 1.0140 - val_accuracy: 0.0000e+00\n",
      "Epoch 131/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.5007 - accuracy: 0.7402 - val_loss: 1.0156 - val_accuracy: 0.0000e+00\n",
      "Epoch 132/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4727 - accuracy: 0.7795 - val_loss: 1.0113 - val_accuracy: 0.0000e+00\n",
      "Epoch 133/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.4849 - accuracy: 0.7717 - val_loss: 1.0101 - val_accuracy: 0.0000e+00\n",
      "Epoch 134/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4797 - accuracy: 0.7795 - val_loss: 1.0138 - val_accuracy: 0.0000e+00\n",
      "Epoch 135/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4876 - accuracy: 0.7874 - val_loss: 1.0106 - val_accuracy: 0.0000e+00\n",
      "Epoch 136/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.5001 - accuracy: 0.7480 - val_loss: 1.0095 - val_accuracy: 0.0000e+00\n",
      "Epoch 137/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4949 - accuracy: 0.7559 - val_loss: 1.0066 - val_accuracy: 0.0000e+00\n",
      "Epoch 138/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.4956 - accuracy: 0.7717 - val_loss: 1.0056 - val_accuracy: 0.0000e+00\n",
      "Epoch 139/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5099 - accuracy: 0.7717 - val_loss: 1.0065 - val_accuracy: 0.0000e+00\n",
      "Epoch 140/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4803 - accuracy: 0.7953 - val_loss: 1.0042 - val_accuracy: 0.0000e+00\n",
      "Epoch 141/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.4694 - accuracy: 0.7717 - val_loss: 0.9985 - val_accuracy: 0.0000e+00\n",
      "Epoch 142/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4922 - accuracy: 0.7638 - val_loss: 0.9953 - val_accuracy: 0.0000e+00\n",
      "Epoch 143/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4490 - accuracy: 0.8189 - val_loss: 0.9980 - val_accuracy: 0.0000e+00\n",
      "Epoch 144/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4636 - accuracy: 0.7795 - val_loss: 0.9995 - val_accuracy: 0.0000e+00\n",
      "Epoch 145/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4770 - accuracy: 0.7480 - val_loss: 1.0019 - val_accuracy: 0.0000e+00\n",
      "Epoch 146/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.4771 - accuracy: 0.7559 - val_loss: 0.9989 - val_accuracy: 0.0000e+00\n",
      "Epoch 147/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4871 - accuracy: 0.7323 - val_loss: 0.9929 - val_accuracy: 0.0000e+00\n",
      "Epoch 148/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4653 - accuracy: 0.7638 - val_loss: 0.9902 - val_accuracy: 0.0000e+00\n",
      "Epoch 149/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4673 - accuracy: 0.7795 - val_loss: 0.9918 - val_accuracy: 0.0000e+00\n",
      "Epoch 150/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4884 - accuracy: 0.7480 - val_loss: 0.9876 - val_accuracy: 0.0000e+00\n",
      "Epoch 151/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5012 - accuracy: 0.7874 - val_loss: 0.9879 - val_accuracy: 0.0000e+00\n",
      "Epoch 152/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4771 - accuracy: 0.7953 - val_loss: 0.9879 - val_accuracy: 0.0000e+00\n",
      "Epoch 153/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4416 - accuracy: 0.8110 - val_loss: 0.9891 - val_accuracy: 0.0000e+00\n",
      "Epoch 154/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4685 - accuracy: 0.7717 - val_loss: 0.9868 - val_accuracy: 0.0000e+00\n",
      "Epoch 155/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4809 - accuracy: 0.7480 - val_loss: 0.9892 - val_accuracy: 0.0000e+00\n",
      "Epoch 156/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4708 - accuracy: 0.7638 - val_loss: 0.9820 - val_accuracy: 0.0000e+00\n",
      "Epoch 157/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4287 - accuracy: 0.7953 - val_loss: 0.9809 - val_accuracy: 0.0000e+00\n",
      "Epoch 158/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4771 - accuracy: 0.7717 - val_loss: 0.9805 - val_accuracy: 0.0000e+00\n",
      "Epoch 159/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4377 - accuracy: 0.7874 - val_loss: 0.9804 - val_accuracy: 0.0000e+00\n",
      "Epoch 160/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4184 - accuracy: 0.8031 - val_loss: 0.9819 - val_accuracy: 0.0000e+00\n",
      "Epoch 161/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4490 - accuracy: 0.7559 - val_loss: 0.9786 - val_accuracy: 0.0000e+00\n",
      "Epoch 162/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4717 - accuracy: 0.7323 - val_loss: 0.9750 - val_accuracy: 0.0000e+00\n",
      "Epoch 163/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4710 - accuracy: 0.7638 - val_loss: 0.9819 - val_accuracy: 0.0000e+00\n",
      "Epoch 164/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4506 - accuracy: 0.8110 - val_loss: 0.9822 - val_accuracy: 0.0000e+00\n",
      "Epoch 165/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4940 - accuracy: 0.7244 - val_loss: 0.9849 - val_accuracy: 0.0000e+00\n",
      "Epoch 166/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4800 - accuracy: 0.7638 - val_loss: 0.9831 - val_accuracy: 0.0000e+00\n",
      "Epoch 167/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4922 - accuracy: 0.7480 - val_loss: 0.9819 - val_accuracy: 0.0000e+00\n",
      "Epoch 168/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4653 - accuracy: 0.7717 - val_loss: 0.9816 - val_accuracy: 0.0000e+00\n",
      "Epoch 169/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4481 - accuracy: 0.7795 - val_loss: 0.9818 - val_accuracy: 0.0000e+00\n",
      "Epoch 170/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4607 - accuracy: 0.7953 - val_loss: 0.9820 - val_accuracy: 0.0000e+00\n",
      "Epoch 171/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4667 - accuracy: 0.7480 - val_loss: 0.9806 - val_accuracy: 0.0000e+00\n",
      "Epoch 172/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4814 - accuracy: 0.7480 - val_loss: 0.9808 - val_accuracy: 0.0000e+00\n",
      "Epoch 173/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.4581 - accuracy: 0.7874 - val_loss: 0.9743 - val_accuracy: 0.0000e+00\n",
      "Epoch 174/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4491 - accuracy: 0.7402 - val_loss: 0.9761 - val_accuracy: 0.0000e+00\n",
      "Epoch 175/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4609 - accuracy: 0.7717 - val_loss: 0.9745 - val_accuracy: 0.0000e+00\n",
      "Epoch 176/500\n",
      "127/127 [==============================] - 0s 94us/step - loss: 0.4222 - accuracy: 0.7874 - val_loss: 0.9753 - val_accuracy: 0.0000e+00\n",
      "Epoch 177/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.4695 - accuracy: 0.7795 - val_loss: 0.9761 - val_accuracy: 0.0000e+00\n",
      "Epoch 178/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.4361 - accuracy: 0.8110 - val_loss: 0.9786 - val_accuracy: 0.0000e+00\n",
      "Epoch 179/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.4511 - accuracy: 0.7953 - val_loss: 0.9720 - val_accuracy: 0.0000e+00\n",
      "Epoch 180/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4264 - accuracy: 0.8031 - val_loss: 0.9725 - val_accuracy: 0.0000e+00\n",
      "Epoch 181/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4368 - accuracy: 0.8110 - val_loss: 0.9747 - val_accuracy: 0.0000e+00\n",
      "Epoch 182/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4419 - accuracy: 0.8110 - val_loss: 0.9766 - val_accuracy: 0.0000e+00\n",
      "Epoch 183/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4675 - accuracy: 0.7874 - val_loss: 0.9705 - val_accuracy: 0.0000e+00\n",
      "Epoch 184/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4573 - accuracy: 0.7480 - val_loss: 0.9698 - val_accuracy: 0.0000e+00\n",
      "Epoch 185/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4273 - accuracy: 0.7717 - val_loss: 0.9689 - val_accuracy: 0.0000e+00\n",
      "Epoch 186/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4525 - accuracy: 0.7795 - val_loss: 0.9722 - val_accuracy: 0.0000e+00\n",
      "Epoch 187/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4484 - accuracy: 0.8110 - val_loss: 0.9693 - val_accuracy: 0.0000e+00\n",
      "Epoch 188/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.4450 - accuracy: 0.7559 - val_loss: 0.9675 - val_accuracy: 0.0000e+00\n",
      "Epoch 189/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4374 - accuracy: 0.7795 - val_loss: 0.9671 - val_accuracy: 0.0000e+00\n",
      "Epoch 190/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4690 - accuracy: 0.7638 - val_loss: 0.9665 - val_accuracy: 0.0000e+00\n",
      "Epoch 191/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4500 - accuracy: 0.7480 - val_loss: 0.9635 - val_accuracy: 0.0000e+00\n",
      "Epoch 192/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4719 - accuracy: 0.7480 - val_loss: 0.9643 - val_accuracy: 0.0000e+00\n",
      "Epoch 193/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4504 - accuracy: 0.7480 - val_loss: 0.9614 - val_accuracy: 0.0000e+00\n",
      "Epoch 194/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4419 - accuracy: 0.7953 - val_loss: 0.9628 - val_accuracy: 0.0000e+00\n",
      "Epoch 195/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4413 - accuracy: 0.7795 - val_loss: 0.9604 - val_accuracy: 0.0000e+00\n",
      "Epoch 196/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4635 - accuracy: 0.7638 - val_loss: 0.9595 - val_accuracy: 0.0000e+00\n",
      "Epoch 197/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4723 - accuracy: 0.7480 - val_loss: 0.9628 - val_accuracy: 0.0000e+00\n",
      "Epoch 198/500\n",
      "127/127 [==============================] - 0s 94us/step - loss: 0.4499 - accuracy: 0.7717 - val_loss: 0.9620 - val_accuracy: 0.0000e+00\n",
      "Epoch 199/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.4358 - accuracy: 0.7953 - val_loss: 0.9649 - val_accuracy: 0.0000e+00\n",
      "Epoch 200/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4346 - accuracy: 0.7874 - val_loss: 0.9643 - val_accuracy: 0.0000e+00\n",
      "Epoch 201/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4392 - accuracy: 0.7874 - val_loss: 0.9551 - val_accuracy: 0.0000e+00\n",
      "Epoch 202/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4467 - accuracy: 0.7874 - val_loss: 0.9537 - val_accuracy: 0.0000e+00\n",
      "Epoch 203/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4372 - accuracy: 0.7874 - val_loss: 0.9582 - val_accuracy: 0.0000e+00\n",
      "Epoch 204/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4628 - accuracy: 0.7717 - val_loss: 0.9630 - val_accuracy: 0.0000e+00\n",
      "Epoch 205/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4384 - accuracy: 0.7953 - val_loss: 0.9635 - val_accuracy: 0.0000e+00\n",
      "Epoch 206/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4483 - accuracy: 0.7638 - val_loss: 0.9665 - val_accuracy: 0.0000e+00\n",
      "Epoch 207/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4364 - accuracy: 0.7638 - val_loss: 0.9640 - val_accuracy: 0.0000e+00\n",
      "Epoch 208/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4113 - accuracy: 0.8189 - val_loss: 0.9643 - val_accuracy: 0.0000e+00\n",
      "Epoch 209/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4490 - accuracy: 0.7953 - val_loss: 0.9627 - val_accuracy: 0.0000e+00\n",
      "Epoch 210/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4585 - accuracy: 0.7559 - val_loss: 0.9600 - val_accuracy: 0.0000e+00\n",
      "Epoch 211/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4428 - accuracy: 0.7874 - val_loss: 0.9565 - val_accuracy: 0.0000e+00\n",
      "Epoch 212/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4454 - accuracy: 0.7323 - val_loss: 0.9517 - val_accuracy: 0.0000e+00\n",
      "Epoch 213/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4440 - accuracy: 0.7874 - val_loss: 0.9509 - val_accuracy: 0.0000e+00\n",
      "Epoch 214/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4303 - accuracy: 0.7795 - val_loss: 0.9527 - val_accuracy: 0.0000e+00\n",
      "Epoch 215/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.4380 - accuracy: 0.7953 - val_loss: 0.9549 - val_accuracy: 0.0000e+00\n",
      "Epoch 216/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4194 - accuracy: 0.7717 - val_loss: 0.9542 - val_accuracy: 0.0000e+00\n",
      "Epoch 217/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4167 - accuracy: 0.8110 - val_loss: 0.9528 - val_accuracy: 0.0000e+00\n",
      "Epoch 218/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4677 - accuracy: 0.7559 - val_loss: 0.9515 - val_accuracy: 0.0000e+00\n",
      "Epoch 219/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.4381 - accuracy: 0.7717 - val_loss: 0.9508 - val_accuracy: 0.0000e+00\n",
      "Epoch 220/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.4364 - accuracy: 0.7795 - val_loss: 0.9535 - val_accuracy: 0.0000e+00\n",
      "Epoch 221/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4100 - accuracy: 0.8031 - val_loss: 0.9529 - val_accuracy: 0.0000e+00\n",
      "Epoch 222/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4211 - accuracy: 0.7953 - val_loss: 0.9553 - val_accuracy: 0.0000e+00\n",
      "Epoch 223/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4066 - accuracy: 0.7717 - val_loss: 0.9533 - val_accuracy: 0.0000e+00\n",
      "Epoch 224/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4549 - accuracy: 0.8031 - val_loss: 0.9543 - val_accuracy: 0.0000e+00\n",
      "Epoch 225/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4219 - accuracy: 0.7874 - val_loss: 0.9539 - val_accuracy: 0.0000e+00\n",
      "Epoch 226/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4251 - accuracy: 0.7874 - val_loss: 0.9514 - val_accuracy: 0.0000e+00\n",
      "Epoch 227/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3971 - accuracy: 0.7795 - val_loss: 0.9484 - val_accuracy: 0.0000e+00\n",
      "Epoch 228/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4417 - accuracy: 0.7874 - val_loss: 0.9476 - val_accuracy: 0.0000e+00\n",
      "Epoch 229/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4172 - accuracy: 0.7953 - val_loss: 0.9521 - val_accuracy: 0.0000e+00\n",
      "Epoch 230/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3974 - accuracy: 0.7795 - val_loss: 0.9565 - val_accuracy: 0.0000e+00\n",
      "Epoch 231/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4132 - accuracy: 0.8031 - val_loss: 0.9530 - val_accuracy: 0.0000e+00\n",
      "Epoch 232/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4345 - accuracy: 0.7874 - val_loss: 0.9534 - val_accuracy: 0.0000e+00\n",
      "Epoch 233/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4081 - accuracy: 0.8346 - val_loss: 0.9510 - val_accuracy: 0.0000e+00\n",
      "Epoch 234/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4331 - accuracy: 0.7795 - val_loss: 0.9533 - val_accuracy: 0.0000e+00\n",
      "Epoch 235/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4298 - accuracy: 0.7874 - val_loss: 0.9558 - val_accuracy: 0.0000e+00\n",
      "Epoch 236/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4238 - accuracy: 0.7717 - val_loss: 0.9523 - val_accuracy: 0.0000e+00\n",
      "Epoch 237/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4363 - accuracy: 0.7795 - val_loss: 0.9568 - val_accuracy: 0.0000e+00\n",
      "Epoch 238/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4098 - accuracy: 0.7953 - val_loss: 0.9539 - val_accuracy: 0.0000e+00\n",
      "Epoch 239/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4120 - accuracy: 0.7874 - val_loss: 0.9529 - val_accuracy: 0.0000e+00\n",
      "Epoch 240/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.4336 - accuracy: 0.7717 - val_loss: 0.9543 - val_accuracy: 0.0000e+00\n",
      "Epoch 241/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.4111 - accuracy: 0.8268 - val_loss: 0.9564 - val_accuracy: 0.0000e+00\n",
      "Epoch 242/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4300 - accuracy: 0.8268 - val_loss: 0.9605 - val_accuracy: 0.0000e+00\n",
      "Epoch 243/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4325 - accuracy: 0.8031 - val_loss: 0.9607 - val_accuracy: 0.0000e+00\n",
      "Epoch 244/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4302 - accuracy: 0.8031 - val_loss: 0.9585 - val_accuracy: 0.0000e+00\n",
      "Epoch 245/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4172 - accuracy: 0.7795 - val_loss: 0.9565 - val_accuracy: 0.0000e+00\n",
      "Epoch 246/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4229 - accuracy: 0.7795 - val_loss: 0.9547 - val_accuracy: 0.0000e+00\n",
      "Epoch 247/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4076 - accuracy: 0.8031 - val_loss: 0.9543 - val_accuracy: 0.0000e+00\n",
      "Epoch 248/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4256 - accuracy: 0.7559 - val_loss: 0.9528 - val_accuracy: 0.0000e+00\n",
      "Epoch 249/500\n",
      "127/127 [==============================] - 0s 79us/step - loss: 0.4036 - accuracy: 0.8346 - val_loss: 0.9545 - val_accuracy: 0.0000e+00\n",
      "Epoch 250/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4174 - accuracy: 0.7953 - val_loss: 0.9522 - val_accuracy: 0.0000e+00\n",
      "Epoch 251/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4185 - accuracy: 0.7795 - val_loss: 0.9479 - val_accuracy: 0.0000e+00\n",
      "Epoch 252/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4326 - accuracy: 0.7559 - val_loss: 0.9490 - val_accuracy: 0.0000e+00\n",
      "Epoch 253/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4001 - accuracy: 0.7953 - val_loss: 0.9493 - val_accuracy: 0.0000e+00\n",
      "Epoch 254/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4196 - accuracy: 0.8031 - val_loss: 0.9458 - val_accuracy: 0.0000e+00\n",
      "Epoch 255/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4256 - accuracy: 0.7638 - val_loss: 0.9423 - val_accuracy: 0.0000e+00\n",
      "Epoch 256/500\n",
      "127/127 [==============================] - 0s 86us/step - loss: 0.4107 - accuracy: 0.7953 - val_loss: 0.9395 - val_accuracy: 0.0000e+00\n",
      "Epoch 257/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.4002 - accuracy: 0.8031 - val_loss: 0.9381 - val_accuracy: 0.0000e+00\n",
      "Epoch 258/500\n",
      "127/127 [==============================] - 0s 94us/step - loss: 0.4093 - accuracy: 0.7795 - val_loss: 0.9401 - val_accuracy: 0.0000e+00\n",
      "Epoch 259/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3962 - accuracy: 0.7638 - val_loss: 0.9411 - val_accuracy: 0.0000e+00\n",
      "Epoch 260/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.4392 - accuracy: 0.7480 - val_loss: 0.9414 - val_accuracy: 0.0000e+00\n",
      "Epoch 261/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4128 - accuracy: 0.7874 - val_loss: 0.9409 - val_accuracy: 0.0000e+00\n",
      "Epoch 262/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4102 - accuracy: 0.7874 - val_loss: 0.9406 - val_accuracy: 0.0000e+00\n",
      "Epoch 263/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4034 - accuracy: 0.8031 - val_loss: 0.9396 - val_accuracy: 0.0000e+00\n",
      "Epoch 264/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4367 - accuracy: 0.7795 - val_loss: 0.9388 - val_accuracy: 0.0000e+00\n",
      "Epoch 265/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3806 - accuracy: 0.8346 - val_loss: 0.9413 - val_accuracy: 0.0000e+00\n",
      "Epoch 266/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4325 - accuracy: 0.7795 - val_loss: 0.9429 - val_accuracy: 0.0000e+00\n",
      "Epoch 267/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4330 - accuracy: 0.7953 - val_loss: 0.9406 - val_accuracy: 0.0000e+00\n",
      "Epoch 268/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4104 - accuracy: 0.7717 - val_loss: 0.9422 - val_accuracy: 0.0000e+00\n",
      "Epoch 269/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3876 - accuracy: 0.7874 - val_loss: 0.9429 - val_accuracy: 0.0000e+00\n",
      "Epoch 270/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3988 - accuracy: 0.8110 - val_loss: 0.9443 - val_accuracy: 0.0000e+00\n",
      "Epoch 271/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4268 - accuracy: 0.7795 - val_loss: 0.9439 - val_accuracy: 0.0000e+00\n",
      "Epoch 272/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4154 - accuracy: 0.7480 - val_loss: 0.9455 - val_accuracy: 0.0000e+00\n",
      "Epoch 273/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4291 - accuracy: 0.7638 - val_loss: 0.9434 - val_accuracy: 0.0000e+00\n",
      "Epoch 274/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3873 - accuracy: 0.8110 - val_loss: 0.9414 - val_accuracy: 0.0000e+00\n",
      "Epoch 275/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4093 - accuracy: 0.7953 - val_loss: 0.9442 - val_accuracy: 0.0000e+00\n",
      "Epoch 276/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.4325 - accuracy: 0.7795 - val_loss: 0.9457 - val_accuracy: 0.0000e+00\n",
      "Epoch 277/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4037 - accuracy: 0.7874 - val_loss: 0.9472 - val_accuracy: 0.0000e+00\n",
      "Epoch 278/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4083 - accuracy: 0.8110 - val_loss: 0.9483 - val_accuracy: 0.0000e+00\n",
      "Epoch 279/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4041 - accuracy: 0.8031 - val_loss: 0.9438 - val_accuracy: 0.0000e+00\n",
      "Epoch 280/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4152 - accuracy: 0.7874 - val_loss: 0.9459 - val_accuracy: 0.0000e+00\n",
      "Epoch 281/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4158 - accuracy: 0.8031 - val_loss: 0.9448 - val_accuracy: 0.0000e+00\n",
      "Epoch 282/500\n",
      "127/127 [==============================] - 0s 79us/step - loss: 0.4130 - accuracy: 0.8031 - val_loss: 0.9469 - val_accuracy: 0.0000e+00\n",
      "Epoch 283/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4207 - accuracy: 0.7717 - val_loss: 0.9414 - val_accuracy: 0.0000e+00\n",
      "Epoch 284/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.4243 - accuracy: 0.7795 - val_loss: 0.9400 - val_accuracy: 0.0000e+00\n",
      "Epoch 285/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3983 - accuracy: 0.8031 - val_loss: 0.9358 - val_accuracy: 0.0000e+00\n",
      "Epoch 286/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3954 - accuracy: 0.8189 - val_loss: 0.9389 - val_accuracy: 0.0000e+00\n",
      "Epoch 287/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4005 - accuracy: 0.7953 - val_loss: 0.9366 - val_accuracy: 0.0000e+00\n",
      "Epoch 288/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3873 - accuracy: 0.8031 - val_loss: 0.9345 - val_accuracy: 0.0000e+00\n",
      "Epoch 289/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4071 - accuracy: 0.8031 - val_loss: 0.9343 - val_accuracy: 0.0000e+00\n",
      "Epoch 290/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4345 - accuracy: 0.7874 - val_loss: 0.9340 - val_accuracy: 0.0000e+00\n",
      "Epoch 291/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4157 - accuracy: 0.7953 - val_loss: 0.9323 - val_accuracy: 0.0000e+00\n",
      "Epoch 292/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3968 - accuracy: 0.7874 - val_loss: 0.9314 - val_accuracy: 0.0000e+00\n",
      "Epoch 293/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3870 - accuracy: 0.8268 - val_loss: 0.9338 - val_accuracy: 0.0000e+00\n",
      "Epoch 294/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4207 - accuracy: 0.7717 - val_loss: 0.9305 - val_accuracy: 0.0000e+00\n",
      "Epoch 295/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.4086 - accuracy: 0.8110 - val_loss: 0.9269 - val_accuracy: 0.0000e+00\n",
      "Epoch 296/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4062 - accuracy: 0.7953 - val_loss: 0.9267 - val_accuracy: 0.0000e+00\n",
      "Epoch 297/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3933 - accuracy: 0.8189 - val_loss: 0.9263 - val_accuracy: 0.0000e+00\n",
      "Epoch 298/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3957 - accuracy: 0.7953 - val_loss: 0.9254 - val_accuracy: 0.0000e+00\n",
      "Epoch 299/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4100 - accuracy: 0.8189 - val_loss: 0.9229 - val_accuracy: 0.0000e+00\n",
      "Epoch 300/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4160 - accuracy: 0.7795 - val_loss: 0.9203 - val_accuracy: 0.0000e+00\n",
      "Epoch 301/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.4037 - accuracy: 0.7874 - val_loss: 0.9193 - val_accuracy: 0.0000e+00\n",
      "Epoch 302/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4001 - accuracy: 0.7953 - val_loss: 0.9193 - val_accuracy: 0.0000e+00\n",
      "Epoch 303/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4227 - accuracy: 0.7795 - val_loss: 0.9213 - val_accuracy: 0.0000e+00\n",
      "Epoch 304/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.4283 - accuracy: 0.7638 - val_loss: 0.9228 - val_accuracy: 0.0000e+00\n",
      "Epoch 305/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4015 - accuracy: 0.7874 - val_loss: 0.9229 - val_accuracy: 0.0000e+00\n",
      "Epoch 306/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.4162 - accuracy: 0.7953 - val_loss: 0.9168 - val_accuracy: 0.0000e+00\n",
      "Epoch 307/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3983 - accuracy: 0.7795 - val_loss: 0.9132 - val_accuracy: 0.0000e+00\n",
      "Epoch 308/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.4171 - accuracy: 0.8031 - val_loss: 0.9115 - val_accuracy: 0.0000e+00\n",
      "Epoch 309/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3996 - accuracy: 0.8189 - val_loss: 0.9094 - val_accuracy: 0.0000e+00\n",
      "Epoch 310/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3943 - accuracy: 0.8031 - val_loss: 0.9113 - val_accuracy: 0.0000e+00\n",
      "Epoch 311/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3997 - accuracy: 0.7795 - val_loss: 0.9125 - val_accuracy: 0.0000e+00\n",
      "Epoch 312/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4040 - accuracy: 0.7874 - val_loss: 0.9070 - val_accuracy: 0.0000e+00\n",
      "Epoch 313/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3874 - accuracy: 0.8268 - val_loss: 0.9054 - val_accuracy: 0.0000e+00\n",
      "Epoch 314/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4135 - accuracy: 0.8110 - val_loss: 0.9038 - val_accuracy: 0.0000e+00\n",
      "Epoch 315/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.4044 - accuracy: 0.7795 - val_loss: 0.9064 - val_accuracy: 0.0000e+00\n",
      "Epoch 316/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3913 - accuracy: 0.8031 - val_loss: 0.9047 - val_accuracy: 0.0000e+00\n",
      "Epoch 317/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3887 - accuracy: 0.8110 - val_loss: 0.9036 - val_accuracy: 0.0000e+00\n",
      "Epoch 318/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4166 - accuracy: 0.7717 - val_loss: 0.9028 - val_accuracy: 0.0000e+00\n",
      "Epoch 319/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3948 - accuracy: 0.7874 - val_loss: 0.9016 - val_accuracy: 0.0000e+00\n",
      "Epoch 320/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3974 - accuracy: 0.7953 - val_loss: 0.9040 - val_accuracy: 0.0000e+00\n",
      "Epoch 321/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4216 - accuracy: 0.7953 - val_loss: 0.9046 - val_accuracy: 0.0000e+00\n",
      "Epoch 322/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4249 - accuracy: 0.7717 - val_loss: 0.9080 - val_accuracy: 0.0000e+00\n",
      "Epoch 323/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3920 - accuracy: 0.8189 - val_loss: 0.9094 - val_accuracy: 0.0000e+00\n",
      "Epoch 324/500\n",
      "127/127 [==============================] - 0s 134us/step - loss: 0.4148 - accuracy: 0.7795 - val_loss: 0.9076 - val_accuracy: 0.0000e+00\n",
      "Epoch 325/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3936 - accuracy: 0.7795 - val_loss: 0.9103 - val_accuracy: 0.0000e+00\n",
      "Epoch 326/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4280 - accuracy: 0.7953 - val_loss: 0.9102 - val_accuracy: 0.0000e+00\n",
      "Epoch 327/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3924 - accuracy: 0.8031 - val_loss: 0.9113 - val_accuracy: 0.0000e+00\n",
      "Epoch 328/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3962 - accuracy: 0.7795 - val_loss: 0.9110 - val_accuracy: 0.0000e+00\n",
      "Epoch 329/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3990 - accuracy: 0.7953 - val_loss: 0.9087 - val_accuracy: 0.0000e+00\n",
      "Epoch 330/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4391 - accuracy: 0.7559 - val_loss: 0.9089 - val_accuracy: 0.0000e+00\n",
      "Epoch 331/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4069 - accuracy: 0.7480 - val_loss: 0.9083 - val_accuracy: 0.0000e+00\n",
      "Epoch 332/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4183 - accuracy: 0.7795 - val_loss: 0.9048 - val_accuracy: 0.0000e+00\n",
      "Epoch 333/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4294 - accuracy: 0.7480 - val_loss: 0.9064 - val_accuracy: 0.0000e+00\n",
      "Epoch 334/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3824 - accuracy: 0.8346 - val_loss: 0.9084 - val_accuracy: 0.0000e+00\n",
      "Epoch 335/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3977 - accuracy: 0.7795 - val_loss: 0.9043 - val_accuracy: 0.0000e+00\n",
      "Epoch 336/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4013 - accuracy: 0.7795 - val_loss: 0.9014 - val_accuracy: 0.0000e+00\n",
      "Epoch 337/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3983 - accuracy: 0.8110 - val_loss: 0.8976 - val_accuracy: 0.0000e+00\n",
      "Epoch 338/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3963 - accuracy: 0.8110 - val_loss: 0.9001 - val_accuracy: 0.0000e+00\n",
      "Epoch 339/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3894 - accuracy: 0.8031 - val_loss: 0.8968 - val_accuracy: 0.0000e+00\n",
      "Epoch 340/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4166 - accuracy: 0.7638 - val_loss: 0.9021 - val_accuracy: 0.0000e+00\n",
      "Epoch 341/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4007 - accuracy: 0.7795 - val_loss: 0.9010 - val_accuracy: 0.0000e+00\n",
      "Epoch 342/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3922 - accuracy: 0.7795 - val_loss: 0.8995 - val_accuracy: 0.0000e+00\n",
      "Epoch 343/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4023 - accuracy: 0.8268 - val_loss: 0.9009 - val_accuracy: 0.0000e+00\n",
      "Epoch 344/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3638 - accuracy: 0.8268 - val_loss: 0.8978 - val_accuracy: 0.0000e+00\n",
      "Epoch 345/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4108 - accuracy: 0.7480 - val_loss: 0.8992 - val_accuracy: 0.0000e+00\n",
      "Epoch 346/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3928 - accuracy: 0.7953 - val_loss: 0.9033 - val_accuracy: 0.0000e+00\n",
      "Epoch 347/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3820 - accuracy: 0.8189 - val_loss: 0.9018 - val_accuracy: 0.0000e+00\n",
      "Epoch 348/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3934 - accuracy: 0.7953 - val_loss: 0.8998 - val_accuracy: 0.0000e+00\n",
      "Epoch 349/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3858 - accuracy: 0.7874 - val_loss: 0.8980 - val_accuracy: 0.0000e+00\n",
      "Epoch 350/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3851 - accuracy: 0.8031 - val_loss: 0.8980 - val_accuracy: 0.0000e+00\n",
      "Epoch 351/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3969 - accuracy: 0.7874 - val_loss: 0.8974 - val_accuracy: 0.0000e+00\n",
      "Epoch 352/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4067 - accuracy: 0.7953 - val_loss: 0.8979 - val_accuracy: 0.0000e+00\n",
      "Epoch 353/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4130 - accuracy: 0.7717 - val_loss: 0.8996 - val_accuracy: 0.0000e+00\n",
      "Epoch 354/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4038 - accuracy: 0.7717 - val_loss: 0.8991 - val_accuracy: 0.0000e+00\n",
      "Epoch 355/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3963 - accuracy: 0.7874 - val_loss: 0.8960 - val_accuracy: 0.0000e+00\n",
      "Epoch 356/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4010 - accuracy: 0.7874 - val_loss: 0.8954 - val_accuracy: 0.0000e+00\n",
      "Epoch 357/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3965 - accuracy: 0.7874 - val_loss: 0.8927 - val_accuracy: 0.0000e+00\n",
      "Epoch 358/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3635 - accuracy: 0.8110 - val_loss: 0.8978 - val_accuracy: 0.0000e+00\n",
      "Epoch 359/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3915 - accuracy: 0.8031 - val_loss: 0.8976 - val_accuracy: 0.0000e+00\n",
      "Epoch 360/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3768 - accuracy: 0.8189 - val_loss: 0.8948 - val_accuracy: 0.0000e+00\n",
      "Epoch 361/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3780 - accuracy: 0.8346 - val_loss: 0.8946 - val_accuracy: 0.0000e+00\n",
      "Epoch 362/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3770 - accuracy: 0.8346 - val_loss: 0.8950 - val_accuracy: 0.0000e+00\n",
      "Epoch 363/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.4146 - accuracy: 0.7874 - val_loss: 0.8940 - val_accuracy: 0.0000e+00\n",
      "Epoch 364/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4100 - accuracy: 0.7559 - val_loss: 0.8953 - val_accuracy: 0.0000e+00\n",
      "Epoch 365/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3865 - accuracy: 0.8110 - val_loss: 0.8955 - val_accuracy: 0.0000e+00\n",
      "Epoch 366/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3874 - accuracy: 0.8110 - val_loss: 0.8939 - val_accuracy: 0.0000e+00\n",
      "Epoch 367/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3715 - accuracy: 0.7953 - val_loss: 0.8938 - val_accuracy: 0.0000e+00\n",
      "Epoch 368/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3994 - accuracy: 0.7795 - val_loss: 0.9005 - val_accuracy: 0.0000e+00\n",
      "Epoch 369/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3852 - accuracy: 0.8031 - val_loss: 0.8998 - val_accuracy: 0.0000e+00\n",
      "Epoch 370/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3630 - accuracy: 0.8031 - val_loss: 0.8977 - val_accuracy: 0.0000e+00\n",
      "Epoch 371/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4024 - accuracy: 0.8031 - val_loss: 0.9030 - val_accuracy: 0.0000e+00\n",
      "Epoch 372/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3918 - accuracy: 0.7874 - val_loss: 0.9025 - val_accuracy: 0.0000e+00\n",
      "Epoch 373/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3772 - accuracy: 0.8346 - val_loss: 0.9026 - val_accuracy: 0.0000e+00\n",
      "Epoch 374/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3951 - accuracy: 0.8110 - val_loss: 0.9020 - val_accuracy: 0.0000e+00\n",
      "Epoch 375/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3868 - accuracy: 0.7953 - val_loss: 0.8968 - val_accuracy: 0.0000e+00\n",
      "Epoch 376/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3695 - accuracy: 0.8110 - val_loss: 0.8945 - val_accuracy: 0.0000e+00\n",
      "Epoch 377/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3866 - accuracy: 0.8110 - val_loss: 0.8913 - val_accuracy: 0.0000e+00\n",
      "Epoch 378/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4051 - accuracy: 0.7953 - val_loss: 0.8937 - val_accuracy: 0.0000e+00\n",
      "Epoch 379/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3752 - accuracy: 0.8189 - val_loss: 0.8900 - val_accuracy: 0.0000e+00\n",
      "Epoch 380/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3777 - accuracy: 0.8110 - val_loss: 0.8928 - val_accuracy: 0.0000e+00\n",
      "Epoch 381/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3789 - accuracy: 0.8031 - val_loss: 0.8895 - val_accuracy: 0.0000e+00\n",
      "Epoch 382/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3869 - accuracy: 0.7717 - val_loss: 0.8873 - val_accuracy: 0.0000e+00\n",
      "Epoch 383/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3806 - accuracy: 0.8110 - val_loss: 0.8880 - val_accuracy: 0.0000e+00\n",
      "Epoch 384/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3959 - accuracy: 0.8189 - val_loss: 0.8894 - val_accuracy: 0.0000e+00\n",
      "Epoch 385/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3677 - accuracy: 0.8189 - val_loss: 0.8910 - val_accuracy: 0.0000e+00\n",
      "Epoch 386/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3775 - accuracy: 0.8189 - val_loss: 0.8898 - val_accuracy: 0.0000e+00\n",
      "Epoch 387/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3633 - accuracy: 0.7953 - val_loss: 0.8864 - val_accuracy: 0.0000e+00\n",
      "Epoch 388/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3955 - accuracy: 0.7717 - val_loss: 0.8817 - val_accuracy: 0.0000e+00\n",
      "Epoch 389/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3623 - accuracy: 0.8189 - val_loss: 0.8791 - val_accuracy: 0.0000e+00\n",
      "Epoch 390/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3782 - accuracy: 0.8189 - val_loss: 0.8770 - val_accuracy: 0.0000e+00\n",
      "Epoch 391/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3770 - accuracy: 0.8189 - val_loss: 0.8759 - val_accuracy: 0.0000e+00\n",
      "Epoch 392/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4068 - accuracy: 0.7795 - val_loss: 0.8769 - val_accuracy: 0.0000e+00\n",
      "Epoch 393/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3745 - accuracy: 0.8031 - val_loss: 0.8742 - val_accuracy: 0.0000e+00\n",
      "Epoch 394/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3713 - accuracy: 0.8031 - val_loss: 0.8751 - val_accuracy: 0.0000e+00\n",
      "Epoch 395/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3822 - accuracy: 0.7874 - val_loss: 0.8704 - val_accuracy: 0.0000e+00\n",
      "Epoch 396/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4148 - accuracy: 0.7717 - val_loss: 0.8696 - val_accuracy: 0.0000e+00\n",
      "Epoch 397/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3765 - accuracy: 0.7874 - val_loss: 0.8701 - val_accuracy: 0.0000e+00\n",
      "Epoch 398/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3803 - accuracy: 0.8268 - val_loss: 0.8694 - val_accuracy: 0.0000e+00\n",
      "Epoch 399/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3934 - accuracy: 0.7717 - val_loss: 0.8666 - val_accuracy: 0.0000e+00\n",
      "Epoch 400/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3674 - accuracy: 0.8268 - val_loss: 0.8676 - val_accuracy: 0.0000e+00\n",
      "Epoch 401/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3592 - accuracy: 0.7874 - val_loss: 0.8708 - val_accuracy: 0.0000e+00\n",
      "Epoch 402/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3833 - accuracy: 0.8031 - val_loss: 0.8671 - val_accuracy: 0.0000e+00\n",
      "Epoch 403/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3808 - accuracy: 0.7717 - val_loss: 0.8667 - val_accuracy: 0.0000e+00\n",
      "Epoch 404/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3664 - accuracy: 0.8268 - val_loss: 0.8655 - val_accuracy: 0.0000e+00\n",
      "Epoch 405/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3620 - accuracy: 0.8110 - val_loss: 0.8670 - val_accuracy: 0.0000e+00\n",
      "Epoch 406/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3932 - accuracy: 0.7874 - val_loss: 0.8671 - val_accuracy: 0.0000e+00\n",
      "Epoch 407/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3890 - accuracy: 0.7953 - val_loss: 0.8660 - val_accuracy: 0.0000e+00\n",
      "Epoch 408/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3857 - accuracy: 0.8031 - val_loss: 0.8687 - val_accuracy: 0.0000e+00\n",
      "Epoch 409/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3798 - accuracy: 0.7717 - val_loss: 0.8686 - val_accuracy: 0.0000e+00\n",
      "Epoch 410/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3826 - accuracy: 0.7953 - val_loss: 0.8650 - val_accuracy: 0.0000e+00\n",
      "Epoch 411/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3802 - accuracy: 0.8268 - val_loss: 0.8712 - val_accuracy: 0.0000e+00\n",
      "Epoch 412/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3548 - accuracy: 0.8268 - val_loss: 0.8682 - val_accuracy: 0.0000e+00\n",
      "Epoch 413/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3712 - accuracy: 0.8268 - val_loss: 0.8664 - val_accuracy: 0.0000e+00\n",
      "Epoch 414/500\n",
      "127/127 [==============================] - 0s 86us/step - loss: 0.3982 - accuracy: 0.8031 - val_loss: 0.8612 - val_accuracy: 0.0000e+00\n",
      "Epoch 415/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3671 - accuracy: 0.8346 - val_loss: 0.8606 - val_accuracy: 0.0000e+00\n",
      "Epoch 416/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3622 - accuracy: 0.8346 - val_loss: 0.8645 - val_accuracy: 0.0000e+00\n",
      "Epoch 417/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3834 - accuracy: 0.7795 - val_loss: 0.8630 - val_accuracy: 0.0000e+00\n",
      "Epoch 418/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3692 - accuracy: 0.8268 - val_loss: 0.8611 - val_accuracy: 0.0000e+00\n",
      "Epoch 419/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3850 - accuracy: 0.7717 - val_loss: 0.8595 - val_accuracy: 0.0000e+00\n",
      "Epoch 420/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3874 - accuracy: 0.8268 - val_loss: 0.8569 - val_accuracy: 0.0000e+00\n",
      "Epoch 421/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3806 - accuracy: 0.8031 - val_loss: 0.8552 - val_accuracy: 0.0000e+00\n",
      "Epoch 422/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3904 - accuracy: 0.7795 - val_loss: 0.8528 - val_accuracy: 0.0000e+00\n",
      "Epoch 423/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3689 - accuracy: 0.8268 - val_loss: 0.8485 - val_accuracy: 0.0000e+00\n",
      "Epoch 424/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3769 - accuracy: 0.8189 - val_loss: 0.8443 - val_accuracy: 0.0000e+00\n",
      "Epoch 425/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3853 - accuracy: 0.8031 - val_loss: 0.8451 - val_accuracy: 0.0000e+00\n",
      "Epoch 426/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3475 - accuracy: 0.8110 - val_loss: 0.8435 - val_accuracy: 0.0000e+00\n",
      "Epoch 427/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3643 - accuracy: 0.8110 - val_loss: 0.8419 - val_accuracy: 0.0000e+00\n",
      "Epoch 428/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3988 - accuracy: 0.7795 - val_loss: 0.8392 - val_accuracy: 0.0000e+00\n",
      "Epoch 429/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3718 - accuracy: 0.8504 - val_loss: 0.8417 - val_accuracy: 0.0000e+00\n",
      "Epoch 430/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3515 - accuracy: 0.8346 - val_loss: 0.8434 - val_accuracy: 0.0000e+00\n",
      "Epoch 431/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3770 - accuracy: 0.7795 - val_loss: 0.8459 - val_accuracy: 0.0000e+00\n",
      "Epoch 432/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3828 - accuracy: 0.8110 - val_loss: 0.8486 - val_accuracy: 0.0000e+00\n",
      "Epoch 433/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3781 - accuracy: 0.7953 - val_loss: 0.8479 - val_accuracy: 0.0000e+00\n",
      "Epoch 434/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3755 - accuracy: 0.8110 - val_loss: 0.8448 - val_accuracy: 0.0000e+00\n",
      "Epoch 435/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3827 - accuracy: 0.7874 - val_loss: 0.8431 - val_accuracy: 0.0000e+00\n",
      "Epoch 436/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3650 - accuracy: 0.8268 - val_loss: 0.8426 - val_accuracy: 0.0000e+00\n",
      "Epoch 437/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3705 - accuracy: 0.8189 - val_loss: 0.8422 - val_accuracy: 0.0000e+00\n",
      "Epoch 438/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3491 - accuracy: 0.8504 - val_loss: 0.8435 - val_accuracy: 0.0000e+00\n",
      "Epoch 439/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3944 - accuracy: 0.7953 - val_loss: 0.8431 - val_accuracy: 0.0000e+00\n",
      "Epoch 440/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3749 - accuracy: 0.8110 - val_loss: 0.8415 - val_accuracy: 0.0000e+00\n",
      "Epoch 441/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3625 - accuracy: 0.8268 - val_loss: 0.8406 - val_accuracy: 0.0000e+00\n",
      "Epoch 442/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3821 - accuracy: 0.7953 - val_loss: 0.8444 - val_accuracy: 0.0000e+00\n",
      "Epoch 443/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3766 - accuracy: 0.8189 - val_loss: 0.8441 - val_accuracy: 0.0000e+00\n",
      "Epoch 444/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3857 - accuracy: 0.7953 - val_loss: 0.8432 - val_accuracy: 0.0000e+00\n",
      "Epoch 445/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3818 - accuracy: 0.8031 - val_loss: 0.8424 - val_accuracy: 0.0000e+00\n",
      "Epoch 446/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3725 - accuracy: 0.8189 - val_loss: 0.8386 - val_accuracy: 0.0000e+00\n",
      "Epoch 447/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3906 - accuracy: 0.7953 - val_loss: 0.8372 - val_accuracy: 0.0000e+00\n",
      "Epoch 448/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3475 - accuracy: 0.8425 - val_loss: 0.8363 - val_accuracy: 0.0000e+00\n",
      "Epoch 449/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3879 - accuracy: 0.7717 - val_loss: 0.8358 - val_accuracy: 0.0000e+00\n",
      "Epoch 450/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3629 - accuracy: 0.8504 - val_loss: 0.8335 - val_accuracy: 0.0000e+00\n",
      "Epoch 451/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3305 - accuracy: 0.8504 - val_loss: 0.8315 - val_accuracy: 0.0000e+00\n",
      "Epoch 452/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3892 - accuracy: 0.7953 - val_loss: 0.8280 - val_accuracy: 0.0000e+00\n",
      "Epoch 453/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4112 - accuracy: 0.7795 - val_loss: 0.8273 - val_accuracy: 0.0000e+00\n",
      "Epoch 454/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3888 - accuracy: 0.8189 - val_loss: 0.8267 - val_accuracy: 0.0000e+00\n",
      "Epoch 455/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3408 - accuracy: 0.8346 - val_loss: 0.8238 - val_accuracy: 0.0000e+00\n",
      "Epoch 456/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3549 - accuracy: 0.8189 - val_loss: 0.8275 - val_accuracy: 0.0000e+00\n",
      "Epoch 457/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3729 - accuracy: 0.7795 - val_loss: 0.8231 - val_accuracy: 0.0000e+00\n",
      "Epoch 458/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3552 - accuracy: 0.8110 - val_loss: 0.8211 - val_accuracy: 0.0000e+00\n",
      "Epoch 459/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3602 - accuracy: 0.8268 - val_loss: 0.8214 - val_accuracy: 0.0000e+00\n",
      "Epoch 460/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3463 - accuracy: 0.8425 - val_loss: 0.8196 - val_accuracy: 0.0000e+00\n",
      "Epoch 461/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3384 - accuracy: 0.8504 - val_loss: 0.8190 - val_accuracy: 0.0000e+00\n",
      "Epoch 462/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3813 - accuracy: 0.8268 - val_loss: 0.8192 - val_accuracy: 0.0000e+00\n",
      "Epoch 463/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4022 - accuracy: 0.7795 - val_loss: 0.8200 - val_accuracy: 0.0000e+00\n",
      "Epoch 464/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3348 - accuracy: 0.8268 - val_loss: 0.8149 - val_accuracy: 0.0000e+00\n",
      "Epoch 465/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3331 - accuracy: 0.8268 - val_loss: 0.8158 - val_accuracy: 0.0000e+00\n",
      "Epoch 466/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3601 - accuracy: 0.8346 - val_loss: 0.8167 - val_accuracy: 0.0000e+00\n",
      "Epoch 467/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3442 - accuracy: 0.8425 - val_loss: 0.8179 - val_accuracy: 0.0000e+00\n",
      "Epoch 468/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4037 - accuracy: 0.7638 - val_loss: 0.8177 - val_accuracy: 0.0000e+00\n",
      "Epoch 469/500\n",
      "127/127 [==============================] - 0s 79us/step - loss: 0.3378 - accuracy: 0.8189 - val_loss: 0.8158 - val_accuracy: 0.0000e+00\n",
      "Epoch 470/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3503 - accuracy: 0.8189 - val_loss: 0.8116 - val_accuracy: 0.0000e+00\n",
      "Epoch 471/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3719 - accuracy: 0.8504 - val_loss: 0.8137 - val_accuracy: 0.0000e+00\n",
      "Epoch 472/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3981 - accuracy: 0.7795 - val_loss: 0.8110 - val_accuracy: 0.0000e+00\n",
      "Epoch 473/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3723 - accuracy: 0.7874 - val_loss: 0.8115 - val_accuracy: 0.0000e+00\n",
      "Epoch 474/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3736 - accuracy: 0.8268 - val_loss: 0.8121 - val_accuracy: 0.0000e+00\n",
      "Epoch 475/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3723 - accuracy: 0.7795 - val_loss: 0.8075 - val_accuracy: 0.0000e+00\n",
      "Epoch 476/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3611 - accuracy: 0.7480 - val_loss: 0.8037 - val_accuracy: 0.0000e+00\n",
      "Epoch 477/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3535 - accuracy: 0.8346 - val_loss: 0.8036 - val_accuracy: 0.0000e+00\n",
      "Epoch 478/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3319 - accuracy: 0.8583 - val_loss: 0.8045 - val_accuracy: 0.0000e+00\n",
      "Epoch 479/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3742 - accuracy: 0.7953 - val_loss: 0.7995 - val_accuracy: 0.0000e+00\n",
      "Epoch 480/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3609 - accuracy: 0.8425 - val_loss: 0.7972 - val_accuracy: 0.0000e+00\n",
      "Epoch 481/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3672 - accuracy: 0.8425 - val_loss: 0.7955 - val_accuracy: 0.0000e+00\n",
      "Epoch 482/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4256 - accuracy: 0.7559 - val_loss: 0.7940 - val_accuracy: 0.0000e+00\n",
      "Epoch 483/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3424 - accuracy: 0.8346 - val_loss: 0.7926 - val_accuracy: 0.0000e+00\n",
      "Epoch 484/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3612 - accuracy: 0.8346 - val_loss: 0.7902 - val_accuracy: 0.0000e+00\n",
      "Epoch 485/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3537 - accuracy: 0.8425 - val_loss: 0.7908 - val_accuracy: 0.0000e+00\n",
      "Epoch 486/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3578 - accuracy: 0.7795 - val_loss: 0.7889 - val_accuracy: 0.0435\n",
      "Epoch 487/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3387 - accuracy: 0.8661 - val_loss: 0.7904 - val_accuracy: 0.0000e+00\n",
      "Epoch 488/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3664 - accuracy: 0.8110 - val_loss: 0.7882 - val_accuracy: 0.0435\n",
      "Epoch 489/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3590 - accuracy: 0.7953 - val_loss: 0.7902 - val_accuracy: 0.0435\n",
      "Epoch 490/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3473 - accuracy: 0.8346 - val_loss: 0.7885 - val_accuracy: 0.0435\n",
      "Epoch 491/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3584 - accuracy: 0.8189 - val_loss: 0.7888 - val_accuracy: 0.0435\n",
      "Epoch 492/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3482 - accuracy: 0.8346 - val_loss: 0.7881 - val_accuracy: 0.0435\n",
      "Epoch 493/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3857 - accuracy: 0.8346 - val_loss: 0.7906 - val_accuracy: 0.0435\n",
      "Epoch 494/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3484 - accuracy: 0.8504 - val_loss: 0.7923 - val_accuracy: 0.0435\n",
      "Epoch 495/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3441 - accuracy: 0.8661 - val_loss: 0.7940 - val_accuracy: 0.0435\n",
      "Epoch 496/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3454 - accuracy: 0.8110 - val_loss: 0.7914 - val_accuracy: 0.0435\n",
      "Epoch 497/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3635 - accuracy: 0.8031 - val_loss: 0.7936 - val_accuracy: 0.0435\n",
      "Epoch 498/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3455 - accuracy: 0.8504 - val_loss: 0.7955 - val_accuracy: 0.0435\n",
      "Epoch 499/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3505 - accuracy: 0.8425 - val_loss: 0.7987 - val_accuracy: 0.0000e+00\n",
      "Epoch 500/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3600 - accuracy: 0.8425 - val_loss: 0.7988 - val_accuracy: 0.0000e+00\n",
      "Test accuracy : 66.67%\n"
     ]
    }
   ],
   "source": [
    "#Adagrad+dropout\n",
    "model_drop = Sequential()\n",
    "model_drop.add(Dense(20,input_dim=4,activation='sigmoid'))\n",
    "# 隐层\n",
    "model_drop.add(Dropout(0.2))\n",
    "model_drop.add(Dense(20, activation='sigmoid',input_dim=20))  # Dense层为中间层\n",
    "model_drop.add(Dropout(0.2))\n",
    "model_drop.add(Dense(20, activation='sigmoid',input_dim=20))  # Dense层为中间层\n",
    "model_drop.add(Dropout(0.2))\n",
    "# 输出层\n",
    "model_drop.add(Dense(3, input_dim=20,activation='softmax'))\n",
    "ada=optimizers.Adagrad(learning_rate=0.01)\n",
    "model_drop.compile(loss='categorical_crossentropy', optimizer=ada,metrics=['accuracy'])\n",
    "history_ada_drop=model_drop.fit(input_data,correct_data,validation_split=0.15,epochs=500)\n",
    "ans_ada_drop=model_drop.predict(input_test)\n",
    "# model_drop.summary()\n",
    "# ansada_dro=model_drop.predict(input_test)\n",
    "# outlist=[]\n",
    "# for i in range(len(input_data)):\n",
    "#     a=ansada_dro[i].argmax()\n",
    "#     outlist.append(a)\n",
    "# out=pd.DataFrame(outlist)\n",
    "# out.value_counts()\n",
    "score2=model_drop.evaluate(input_test,correct_test,verbose=0)\n",
    "print(\"Test accuracy : %.2f%%\" %(score2[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# correct_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    25\n",
       "1    25\n",
       "0    25\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlist=[]\n",
    "for i in range(len(index_test)):\n",
    "    a=ans_ada[i].argmax()\n",
    "    outlist.append(a)\n",
    "out=pd.DataFrame(outlist)\n",
    "out.value_counts()#ada对测试集的分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    50\n",
       "0    25\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlist=[]\n",
    "for i in range(len(index_test)):\n",
    "    a=ans_ada_drop[i].argmax()\n",
    "    outlist.append(a)\n",
    "out=pd.DataFrame(outlist)\n",
    "out.value_counts()#ada+drop对测试集的分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAVdElEQVR4nO3dfZBd9X3f8fd3n/UsQEJgPSA5ETbCz97BOLYbamwiGBeacdqBhHEy40RtJ3Tcxk0GNy1JcKczTlM7aUuawMTjNJOG4rh1NK5a/IRdT5sYBMY2EpYsy4AkgyVA3Avo3tXd3V//uGfJZR+vtHf33HPu+zWzo3setPv9Sed8dPQ7v3N+kVJCklR8fXkXIEnqDANdkkrCQJekkjDQJakkDHRJKomBvH7whg0b0vbt2/P68ZJUSA8//PCzKaWNs23LLdC3b9/O/v378/rxklRIEfHkXNvscpGkkjDQJakkDHRJKgkDXZJKwkCXpJIw0CWpJAx0SSqJ3MahS9L5Olmt8xcPHmNicjLvUs7LtVds4s1b13f8+xrokgrnc4+c4FNfPgxARM7FnIeL144Y6JIE8ELtLEMDfRz+N9fnXUpXsQ9dUuFUaw3WjgzmXUbXMdAlFU61Ns66FXYwTGegSyqcSq3BuhVeoU9noEsqnGq9wVoDfQYDXVLheIU+OwNdUuFUvCk6K+8qSGpbtd6g3pjIt4jUHOXiFfpMBrqktjzx7Mtc+8mvMzGZ8i4FgAtXDeVdQtcx0CW15annzzAxmfhHf+e1bL1wZa61DPQF17/x0lxr6EYGuqS2VGoNAD749i1cvmlNztVoNt4UldSWqUC377p7GeiS2lKtG+jdzkCX1JZKrcFQfx/DA8ZGt/JvRlJbqrVx1q4YJIr4vtoeYaBLaktz7LfjKLqZgS6pLb4/pfv5z600zSe/dJhDz1TzLqPrfPdEhbcswSw76hwDXWoxMZn4j1/9PhetGmLD6uG8y+kql6wd4WeuvCTvMjQPA11q8VJ9nJTgH//0T/DL73lt3uVI58Q+dKmFD8+oyNoK9IjYHRGHIuJIRNw+y/ZtEfFARHwrIr4TETd0vlRp6fnwjIpswUCPiH7gLuB6YBdwS0TsmrbbvwLuSym9FbgZ+MNOFyoth6krdEdzqIjauUK/CjiSUjqaUjoL3AvcNG2fBKzNPq8DftS5EqXlU7XLRQXWTqBvBo61LB/P1rX6beDWiDgO7AP+6WzfKCL2RMT+iNh/6tSp8yhXWlr2oavIOnVT9BbgMymlLcANwJ9FxIzvnVK6O6U0mlIa3bhxY4d+tNQ5U33odrmoiNoZtngC2NqyvCVb1+rDwG6AlNJfR8QIsAE42YkipcWamEw88tRpxhqT8+538EdV+vuCVUP9y1SZ1DntBPpDwM6I2EEzyG8Gfn7aPk8B1wKfiYgrgBHAPhV1ja9+7yS/8l/2t7Xv5vUrfAGVCmnBQE8pjUfEbcD9QD/w6ZTSgYi4E9ifUtoLfBS4JyL+Oc0bpL+UUuqOiQcl4JlqHYB7PjTK+pXzd6dsuWDFcpQkdVxbT4qmlPbRvNnZuu6Ols8HgXd1tjSpc6ZGr7xn5wZGBu1OUTn5pKh6QrXWYHigzzBXqRno6gmVmq9+VfkZ6OoJ1XrDseUqPQNdPaFSa7B2xJeLqtwMdPWEam3cK3SVnoGunmAfunqB/wdVYf3g1Ev86p8/Qr0xseC+x0+f4ZrX+boJlZuBrsL69rEX+N4zL/K+Kzaxanj+4Yhv3XYBH3zblmWqTMqHga7CmnpY6BMffCMXOf+nZB+6iqtSGwd8M6I0xUBXYVVqDVYN9TPY72EsgYGuAqvWHbkitTLQVViVmk9/Sq0MdBWWY8ulVzPQVVjVWoO1Iwa6NMVhi+p6lTMNPv/oCRoTr54+7plqnStfsy6nqqTuY6Cr633+0RP81t4Ds267fNPqZa5G6l4Gurre8y+fBeDRO95PX9/fzvUZwBq7XKRXGOjqepVagzXDA6xfOZR3KVJX86aoup7jzaX2GOjqelWHJ0ptMdDV9ZqTU9g7KC3EQFfXqzjeXGqLga6u5wTPUnv8f6yW3fjEJBMptb2/j/hL7THQtayeeu4M1/3+16k3JhfeucUFKw10aSEGupbV0Wdfot6Y5Nart3HpuhVt/Z6BvuBn37Z5iSuTis9A17KqZNPG/dJPbecnL16TczVSuXhTVMtqah5Q+8SlzjPQtayq9WweUIchSh1noGtZVWoNhgf6GBnsz7sUqXQMdC2rqtPGSUvGQNeych5QaekY6FpWvjlRWjptBXpE7I6IQxFxJCJun2OffxgRByPiQET8186WqSKr1hv81l89xq9/9ts8/vSLXqFLS2TBcegR0Q/cBbwfOA48FBF7U0oHW/bZCXwMeFdK6XREXLxUBat4Hjz6PH/610+yYfUwIwN9vGfnhrxLkkqpnQeLrgKOpJSOAkTEvcBNwMGWfX4FuCuldBogpXSy04WquKYeJvrcP3knl120KudqpPJqp8tlM3CsZfl4tq7V5cDlEfF/I+JvImL3bN8oIvZExP6I2H/q1Knzq1iFMxXodrVIS6tTN0UHgJ3ANcAtwD0RsX76Timlu1NKoyml0Y0bN3boR6vbTQW6EzpLS6udQD8BbG1Z3pKta3Uc2JtSaqSUfggcphnwEtV6c5Ln/r7IuxSp1NoJ9IeAnRGxIyKGgJuBvdP2+TzNq3MiYgPNLpijnStTReb7zKXlsWCgp5TGgduA+4HHgftSSgci4s6IuDHb7X7guYg4CDwA/HpK6bmlKlrF4iTP0vJo6/W5KaV9wL5p6+5o+ZyAX8u+pFdxkmdpeXiWqSPGxic4/MxLJGZOLXfyxTqXb/Ld59JSM9DVEb93/yHu+cYP59z+bh8mkpacga6O+FGlzmvWjfDxv/+GWbePXnbhMlck9R4DXR1RrTXYtG6Ea6/YlHcpUs/ybYvqCF+LK+XPQFdHVGsNp5WTcmagqyO8QpfyZ6Br0VJKVOvjrHWsuZQrA12L9vLZCSYmk1foUs4MdC1aNXubon3oUr4MdC3av//iYcD3nUt5M9C1aPuffB6At2+/IOdKpN5moGvRXqyPc+vV27h4zUjepUg9zUDXoqSUHLIodQkDXYsyNcLFG6JS/gx0LUrVCaClrmGga1GmJoB2RiIpfwa6FsUrdKl7GOhalIoPFUldw5dv6Jz9n8On+HG1DsAjT50GvEKXuoGBrnPy3EtjfOjTD75q3cqhfjasGcqpIklTDHSdk9NnzgLwOzdeyXtffzHQvCG6cshDScqbZ6HOyVSf+fYNq9h64cqcq5HUypuiOid/exPUawGp2xjoOifV2jjgTVCpGxnoOicVx51LXctA1znxyVCpexnoOieVWoOVQ/0M9nvoSN3Gs1LnpFpr+FSo1KUMdLXt8aerfPbh46x2hIvUlQx0te3QMy8C8Avv2JZzJZJmY6CrbVM3RP/em1+TcyWSZmOgq21V36wodTUDXW2r1BqsGOxnaMDDRupGbZ2ZEbE7Ig5FxJGIuH2e/T4YESkiRjtXorpFte5k0FI3WzDQI6IfuAu4HtgF3BIRu2bZbw3wEeCbnS5S3aFSM9ClbtbOFfpVwJGU0tGU0lngXuCmWfb7OPAJoN7B+tRFKrUGa1c4ZFHqVu0E+mbgWMvy8WzdKyLibcDWlNL/nO8bRcSeiNgfEftPnTp1zsUqX9XauFfoUhdb9N2tiOgDPgl8dKF9U0p3p5RGU0qjGzduXOyP1jKZnEz84deOcOz0Gd/hInWxdgL9BLC1ZXlLtm7KGuANwNci4gngamCvN0bL44fPvczv/u9DNCYmeftlF+RdjqQ5tNMh+hCwMyJ20Azym4Gfn9qYUqoAG6aWI+JrwL9IKe3vbKnKywtnmuPP/+jWt3PN6y7OuRpJc1nwCj2lNA7cBtwPPA7cl1I6EBF3RsSNS12g8lf1HehSIbQ1ZCGltA/YN23dHXPse83iy1I3qdZ9B7pUBD7ypwU5S5FUDAa6FuQ7XKRiMNC1IN/hIhWDZ6gW5CP/UjEY6FrQkZMv+ci/VAAGuub19cOneOSpF1g5ZKBL3c5A17yeqdQA+I2feV3OlUhaiIGueY2NTwLwukvW5FyJpIUY6JpXvTEBwPBgf86VSFqIga55jTWaV+jDDlmUup5nqeY1Nj5Jf18w2O+hInU7z1LNq96Y8OpcKgjPVM1rbHzSQJcKwjNV8xobn2B4wBuiUhEY6JpXvTHJyKCHiVQEnqmal1foUnEY6JrX2Pgkw16hS4Xgmap5jTUmGfEKXSoEA13zqo9PeIUuFYRnquY11nDYolQUnqmalzdFpeIw0DWvesObolJReKZqXs0nRb1Cl4rAQNe8ml0uHiZSEXimak4pJeqNCUZ8F7pUCAa65lRvTNKYSE4QLRWEga45VWoNANatGMy5EkntMNA1p2rdQJeKxEDXnKau0NeOGOhSERjomlPVLhepUAx0zck+dKlYDHTN6ZUuFwNdKgQDXXOq1sYBWDvisEWpCAx0zer0y2f51JcPMzzQx0C/h4lUBG2dqRGxOyIORcSRiLh9lu2/FhEHI+I7EfGViLis86VqOf3g1EsAXHflJTlXIqldCwZ6RPQDdwHXA7uAWyJi17TdvgWMppTeBPwl8LudLlTLa6r//JffvSPnSiS1q50r9KuAIymloymls8C9wE2tO6SUHkgpnckW/wbY0tkytdymHiryhqhUHO0E+mbgWMvy8WzdXD4M/K/FFKX8Vc44ZFEqmo4OX4iIW4FR4Kfn2L4H2AOwbdu2Tv5odVglG+GyxhEuUmG0c4V+AtjasrwlW/cqEfE+4DeBG1NKY7N9o5TS3Sml0ZTS6MaNG8+nXi2Tar3BqqF+Bh3hIhVGO2frQ8DOiNgREUPAzcDe1h0i4q3AH9MM85OdL1PLrVJr2N0iFcyCgZ5SGgduA+4HHgfuSykdiIg7I+LGbLd/B6wGPhsRj0bE3jm+nQqiWmt4Q1QqmLY6SFNK+4B909bd0fL5fR2uSzk6/fJZvnjwx1y148K8S5F0Duwg1Qz7HnsagO0Xrcy5EknnwkDXDLWzEwD86w9Mf35MUjcz0DVDvdEMdCeHlorFQNcMY+OT9AUM9EXepUg6Bwa6Zhgbn2R4oJ8IA10qEgNdM9QbE4wMemhIReNZqxnGGs0rdEnFYqBrhrHxCYa9QpcKx7NWM9QbkwwPeGhIReNZqxnGxiccsigVkIGuGZqjXDw0pKLxrNUM9caEN0WlAjLQNcPY+KTDFqUC8qzVDFMPFkkqFgNdMzS7XDw0pKLxrNUMY+OTjkOXCsizVjOMeVNUKiQDXTPUvUKXCqmtKehUPhOTiY9/4SCnXhqbse2sN0WlQjLQe9RTz5/hM//vCTatHWb18KsPg8s3reYdzicqFY6B3qMqtQYA//Zn38i1V2zKuRpJnWBHaY+qZoG+bsVgzpVI6hQDvUdNXaGvNdCl0jDQe1S17hW6VDYGeo+q2OUilY6B3qMqtQZD/X0+4i+ViGdzj6rWxlm7YpCIyLsUSR3isMUe8/jTVSq1Bk8+9zLrVvjXL5WJZ3QPeeq5M1z/B994ZfmnfuKiHKuR1GkGeg/58Yt1AP7lDa/nDZvXcfmmNTlXJKmTDPQeUjnTHNnyjh0X8eat6/MtRlLHeVO0h0yNPfdhIqmcDPQe4thzqdwM9B5SrY0DsGbEnjapjAz0HlKpNVg11M9gv3/tUhm1dWZHxO6IOBQRRyLi9lm2D0fEf8u2fzMitne8Ui1atd6w/1wqsQUDPSL6gbuA64FdwC0RsWvabh8GTqeUfhL4FPCJTheqxavUGvafSyXWTmfqVcCRlNJRgIi4F7gJONiyz03Ab2ef/xL4TxERKaXUwVoBuO+hY9zzjaOd/rY94fjpGm/cvC7vMiQtkXYCfTNwrGX5OPCOufZJKY1HRAW4CHi2daeI2APsAdi2bdt5Fbx+5SA7N60+r9/b63ZuWs0H3vSavMuQtESWdbhDSulu4G6A0dHR87p6v+7KS7juyks6WpcklUE7N0VPAFtblrdk62bdJyIGgHXAc50oUJLUnnYC/SFgZ0TsiIgh4GZg77R99gK/mH3+OeCrS9F/Lkma24JdLlmf+G3A/UA/8OmU0oGIuBPYn1LaC/wJ8GcRcQR4nmboS5KWUVt96CmlfcC+aevuaPlcB/5BZ0uTJJ0LHxmUpJIw0CWpJAx0SSoJA12SSiLyGl0YEaeAJ8/zt29g2lOoPcA29wbb3BsW0+bLUkobZ9uQW6AvRkTsTymN5l3HcrLNvcE294alarNdLpJUEga6JJVEUQP97rwLyIFt7g22uTcsSZsL2YcuSZqpqFfokqRpDHRJKonCBfpCE1YXVUR8OiJORsRjLesujIgvRcT3s18vyNZHRPyH7M/gOxHxtvwqP38RsTUiHoiIgxFxICI+kq0vbbsjYiQiHoyIb2dt/p1s/Y5sgvUj2YTrQ9n6UkzAHhH9EfGtiPhCtlzq9gJExBMR8d2IeDQi9mfrlvTYLlSgtzlhdVF9Btg9bd3twFdSSjuBr2TL0Gz/zuxrD/Cfl6nGThsHPppS2gVcDfxq9vdZ5naPAe9NKb0ZeAuwOyKupjmx+qeyidZP05x4HcozAftHgMdblsve3il/N6X0lpYx50t7bKeUCvMFvBO4v2X5Y8DH8q6rg+3bDjzWsnwIuDT7fClwKPv8x8Ats+1X5C/gr4D390q7gZXAIzTn6H0WGMjWv3Kc05yH4J3Z54Fsv8i79nNs55YsvN4LfAGIMre3pd1PABumrVvSY7tQV+jMPmH15pxqWQ6bUkpPZ5+fATZln0v355D91/qtwDcpebuz7odHgZPAl4AfAC+klMazXVrb9aoJ2IGpCdiL5PeB3wAms+WLKHd7pyTgixHxcETsydYt6bG9rJNE6/yllFJElHKMaUSsBj4H/LOUUjUiXtlWxnanlCaAt0TEeuB/AK/Pt6KlExEfAE6mlB6OiGtyLme5vTuldCIiLga+FBHfa924FMd20a7Q25mwukx+HBGXAmS/nszWl+bPISIGaYb5n6eU/nu2uvTtBkgpvQA8QLPLYX02wTq8ul1Fn4D9XcCNEfEEcC/Nbpc/oLztfUVK6UT260ma/3BfxRIf20UL9HYmrC6T1sm3f5FmH/PU+g9ld8avBiot/40rjGheiv8J8HhK6ZMtm0rb7ojYmF2ZExEraN4zeJxmsP9cttv0Nhd2AvaU0sdSSltSSttpnq9fTSn9AiVt75SIWBURa6Y+A9cBj7HUx3beNw7O40bDDcBhmv2Ov5l3PR1s118ATwMNmv1nH6bZd/gV4PvAl4ELs32D5mifHwDfBUbzrv882/xumv2M3wEezb5uKHO7gTcB38ra/BhwR7b+tcCDwBHgs8Bwtn4kWz6SbX9t3m1YRNuvAb7QC+3N2vft7OvAVFYt9bHto/+SVBJF63KRJM3BQJekkjDQJakkDHRJKgkDXZJKwkCXpJIw0CWpJP4/e4Chh1ixBnwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(range(0,500),history01.history['val_accuracy'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# history01.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对输入数据进行标准化处理\n",
    "question3=[[5.0, 3.5, 1.0, 0.5],[5.5, 2.5, 4.0, 1.0],[7.0, 3.0, 6.0, 2.0],[6.6, 2.5, 1.5, 0.2]]\n",
    "input_data3=question3\n",
    "ave_input3=np.average(input_data3,axis=0)\n",
    "std_input3=np.std(input_data3,axis=0)\n",
    "input_data3=(input_data3-ave_input3)/std_input3\n",
    "#将正确答案转换成独热编码格式\n",
    "n_data3=len(correct) #样本数量\n",
    "correct_data3=np.zeros((n_data3,3))\n",
    "for i in range(n_data3):\n",
    "    correct_data3[i,correct[i]] = 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 0]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对四个数据进行分类\n",
    "ans3=model_ada.predict(input_data3)\n",
    "l2=[]\n",
    "for i in range(4):\n",
    "    a=ans3[i].argmax()\n",
    "    l2.append(a)\n",
    "l2#ada算法结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 127 samples, validate on 23 samples\n",
      "Epoch 1/500\n",
      "127/127 [==============================] - 0s 2ms/step - loss: 1.1155 - accuracy: 0.3228 - val_loss: 1.4688 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/500\n",
      "127/127 [==============================] - 0s 86us/step - loss: 1.0346 - accuracy: 0.4488 - val_loss: 1.4762 - val_accuracy: 0.0000e+00\n",
      "Epoch 3/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0643 - accuracy: 0.4173 - val_loss: 1.4804 - val_accuracy: 0.0000e+00\n",
      "Epoch 4/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0480 - accuracy: 0.4331 - val_loss: 1.5004 - val_accuracy: 0.0000e+00\n",
      "Epoch 5/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0798 - accuracy: 0.4173 - val_loss: 1.5057 - val_accuracy: 0.0000e+00\n",
      "Epoch 6/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0199 - accuracy: 0.5354 - val_loss: 1.4791 - val_accuracy: 0.0000e+00\n",
      "Epoch 7/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0445 - accuracy: 0.4882 - val_loss: 1.4928 - val_accuracy: 0.0000e+00\n",
      "Epoch 8/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0330 - accuracy: 0.4724 - val_loss: 1.5045 - val_accuracy: 0.0000e+00\n",
      "Epoch 9/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 1.0498 - accuracy: 0.4724 - val_loss: 1.4908 - val_accuracy: 0.0000e+00\n",
      "Epoch 10/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0596 - accuracy: 0.4016 - val_loss: 1.4732 - val_accuracy: 0.0000e+00\n",
      "Epoch 11/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0037 - accuracy: 0.5276 - val_loss: 1.4685 - val_accuracy: 0.0000e+00\n",
      "Epoch 12/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.9863 - accuracy: 0.5433 - val_loss: 1.4514 - val_accuracy: 0.0000e+00\n",
      "Epoch 13/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0126 - accuracy: 0.5118 - val_loss: 1.4356 - val_accuracy: 0.0000e+00\n",
      "Epoch 14/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.9752 - accuracy: 0.5748 - val_loss: 1.4217 - val_accuracy: 0.0000e+00\n",
      "Epoch 15/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.9600 - accuracy: 0.5984 - val_loss: 1.4065 - val_accuracy: 0.0000e+00\n",
      "Epoch 16/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.9518 - accuracy: 0.5748 - val_loss: 1.4002 - val_accuracy: 0.0000e+00\n",
      "Epoch 17/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.9840 - accuracy: 0.5118 - val_loss: 1.3972 - val_accuracy: 0.0000e+00\n",
      "Epoch 18/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.9304 - accuracy: 0.5906 - val_loss: 1.3903 - val_accuracy: 0.0000e+00\n",
      "Epoch 19/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.9446 - accuracy: 0.6063 - val_loss: 1.3924 - val_accuracy: 0.0000e+00\n",
      "Epoch 20/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.9519 - accuracy: 0.6457 - val_loss: 1.3800 - val_accuracy: 0.0000e+00\n",
      "Epoch 21/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.9027 - accuracy: 0.6063 - val_loss: 1.3665 - val_accuracy: 0.0000e+00\n",
      "Epoch 22/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 0.9256 - accuracy: 0.5906 - val_loss: 1.3515 - val_accuracy: 0.0000e+00\n",
      "Epoch 23/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.8628 - accuracy: 0.6378 - val_loss: 1.3392 - val_accuracy: 0.0000e+00\n",
      "Epoch 24/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.9203 - accuracy: 0.5906 - val_loss: 1.3239 - val_accuracy: 0.0000e+00\n",
      "Epoch 25/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.8553 - accuracy: 0.6693 - val_loss: 1.3198 - val_accuracy: 0.0000e+00\n",
      "Epoch 26/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.8634 - accuracy: 0.6142 - val_loss: 1.3051 - val_accuracy: 0.0000e+00\n",
      "Epoch 27/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.8900 - accuracy: 0.5984 - val_loss: 1.2969 - val_accuracy: 0.0000e+00\n",
      "Epoch 28/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.8968 - accuracy: 0.5984 - val_loss: 1.2939 - val_accuracy: 0.0000e+00\n",
      "Epoch 29/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.8141 - accuracy: 0.7087 - val_loss: 1.2830 - val_accuracy: 0.0000e+00\n",
      "Epoch 30/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.8221 - accuracy: 0.6457 - val_loss: 1.2587 - val_accuracy: 0.0000e+00\n",
      "Epoch 31/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.8291 - accuracy: 0.6457 - val_loss: 1.2417 - val_accuracy: 0.0000e+00\n",
      "Epoch 32/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.7804 - accuracy: 0.7402 - val_loss: 1.2356 - val_accuracy: 0.0000e+00\n",
      "Epoch 33/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.7747 - accuracy: 0.7087 - val_loss: 1.2168 - val_accuracy: 0.0000e+00\n",
      "Epoch 34/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.7873 - accuracy: 0.6850 - val_loss: 1.2070 - val_accuracy: 0.0000e+00\n",
      "Epoch 35/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.7789 - accuracy: 0.6929 - val_loss: 1.1914 - val_accuracy: 0.0000e+00\n",
      "Epoch 36/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.7518 - accuracy: 0.7165 - val_loss: 1.1863 - val_accuracy: 0.0000e+00\n",
      "Epoch 37/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.7641 - accuracy: 0.7087 - val_loss: 1.1796 - val_accuracy: 0.0000e+00\n",
      "Epoch 38/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.8088 - accuracy: 0.6457 - val_loss: 1.1788 - val_accuracy: 0.0000e+00\n",
      "Epoch 39/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.7276 - accuracy: 0.7165 - val_loss: 1.1769 - val_accuracy: 0.0000e+00\n",
      "Epoch 40/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.7138 - accuracy: 0.7165 - val_loss: 1.1692 - val_accuracy: 0.0000e+00\n",
      "Epoch 41/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.7326 - accuracy: 0.6693 - val_loss: 1.1634 - val_accuracy: 0.0000e+00\n",
      "Epoch 42/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.7042 - accuracy: 0.7323 - val_loss: 1.1532 - val_accuracy: 0.0000e+00\n",
      "Epoch 43/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.7102 - accuracy: 0.7165 - val_loss: 1.1400 - val_accuracy: 0.0000e+00\n",
      "Epoch 44/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.6895 - accuracy: 0.7402 - val_loss: 1.1401 - val_accuracy: 0.0000e+00\n",
      "Epoch 45/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.6879 - accuracy: 0.7480 - val_loss: 1.1359 - val_accuracy: 0.0000e+00\n",
      "Epoch 46/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.6898 - accuracy: 0.7165 - val_loss: 1.1354 - val_accuracy: 0.0000e+00\n",
      "Epoch 47/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.7155 - accuracy: 0.6772 - val_loss: 1.1205 - val_accuracy: 0.0000e+00\n",
      "Epoch 48/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.6443 - accuracy: 0.7638 - val_loss: 1.1149 - val_accuracy: 0.0000e+00\n",
      "Epoch 49/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.6652 - accuracy: 0.7244 - val_loss: 1.1112 - val_accuracy: 0.0000e+00\n",
      "Epoch 50/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.6590 - accuracy: 0.7323 - val_loss: 1.1079 - val_accuracy: 0.0000e+00\n",
      "Epoch 51/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.6364 - accuracy: 0.7795 - val_loss: 1.0983 - val_accuracy: 0.0000e+00\n",
      "Epoch 52/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 0.6290 - accuracy: 0.7480 - val_loss: 1.0879 - val_accuracy: 0.0000e+00\n",
      "Epoch 53/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.7097 - accuracy: 0.6929 - val_loss: 1.0763 - val_accuracy: 0.0000e+00\n",
      "Epoch 54/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.6462 - accuracy: 0.7559 - val_loss: 1.0665 - val_accuracy: 0.0000e+00\n",
      "Epoch 55/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 0.6780 - accuracy: 0.7087 - val_loss: 1.0508 - val_accuracy: 0.0000e+00\n",
      "Epoch 56/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.6241 - accuracy: 0.7402 - val_loss: 1.0430 - val_accuracy: 0.0000e+00\n",
      "Epoch 57/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5955 - accuracy: 0.7638 - val_loss: 1.0439 - val_accuracy: 0.0000e+00\n",
      "Epoch 58/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.5958 - accuracy: 0.7638 - val_loss: 1.0476 - val_accuracy: 0.0000e+00\n",
      "Epoch 59/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.6248 - accuracy: 0.7559 - val_loss: 1.0396 - val_accuracy: 0.0000e+00\n",
      "Epoch 60/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.6112 - accuracy: 0.7559 - val_loss: 1.0368 - val_accuracy: 0.0000e+00\n",
      "Epoch 61/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.6092 - accuracy: 0.7402 - val_loss: 1.0250 - val_accuracy: 0.0000e+00\n",
      "Epoch 62/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5943 - accuracy: 0.7244 - val_loss: 1.0149 - val_accuracy: 0.0000e+00\n",
      "Epoch 63/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5625 - accuracy: 0.7717 - val_loss: 0.9997 - val_accuracy: 0.0000e+00\n",
      "Epoch 64/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5659 - accuracy: 0.7795 - val_loss: 1.0053 - val_accuracy: 0.0000e+00\n",
      "Epoch 65/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.5544 - accuracy: 0.7244 - val_loss: 1.0010 - val_accuracy: 0.0000e+00\n",
      "Epoch 66/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5669 - accuracy: 0.7717 - val_loss: 1.0015 - val_accuracy: 0.0000e+00\n",
      "Epoch 67/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5670 - accuracy: 0.7244 - val_loss: 0.9980 - val_accuracy: 0.0000e+00\n",
      "Epoch 68/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.5664 - accuracy: 0.7402 - val_loss: 0.9949 - val_accuracy: 0.0000e+00\n",
      "Epoch 69/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5609 - accuracy: 0.7795 - val_loss: 0.9840 - val_accuracy: 0.0000e+00\n",
      "Epoch 70/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5857 - accuracy: 0.7323 - val_loss: 0.9864 - val_accuracy: 0.0000e+00\n",
      "Epoch 71/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5603 - accuracy: 0.7559 - val_loss: 0.9965 - val_accuracy: 0.0000e+00\n",
      "Epoch 72/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.5560 - accuracy: 0.7559 - val_loss: 0.9924 - val_accuracy: 0.0000e+00\n",
      "Epoch 73/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5444 - accuracy: 0.7638 - val_loss: 0.9813 - val_accuracy: 0.0000e+00\n",
      "Epoch 74/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5447 - accuracy: 0.7717 - val_loss: 0.9803 - val_accuracy: 0.0000e+00\n",
      "Epoch 75/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.5416 - accuracy: 0.7717 - val_loss: 0.9767 - val_accuracy: 0.0000e+00\n",
      "Epoch 76/500\n",
      "127/127 [==============================] - 0s 51us/step - loss: 0.5839 - accuracy: 0.7165 - val_loss: 0.9864 - val_accuracy: 0.0000e+00\n",
      "Epoch 77/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.5548 - accuracy: 0.8031 - val_loss: 0.9846 - val_accuracy: 0.0000e+00\n",
      "Epoch 78/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5195 - accuracy: 0.8189 - val_loss: 0.9835 - val_accuracy: 0.0000e+00\n",
      "Epoch 79/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.5558 - accuracy: 0.7402 - val_loss: 0.9881 - val_accuracy: 0.0000e+00\n",
      "Epoch 80/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5374 - accuracy: 0.7874 - val_loss: 0.9790 - val_accuracy: 0.0000e+00\n",
      "Epoch 81/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4953 - accuracy: 0.7953 - val_loss: 0.9675 - val_accuracy: 0.0000e+00\n",
      "Epoch 82/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5130 - accuracy: 0.7953 - val_loss: 0.9685 - val_accuracy: 0.0000e+00\n",
      "Epoch 83/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5060 - accuracy: 0.7638 - val_loss: 0.9646 - val_accuracy: 0.0000e+00\n",
      "Epoch 84/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5076 - accuracy: 0.8110 - val_loss: 0.9621 - val_accuracy: 0.0000e+00\n",
      "Epoch 85/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5251 - accuracy: 0.7480 - val_loss: 0.9497 - val_accuracy: 0.0000e+00\n",
      "Epoch 86/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.5146 - accuracy: 0.7717 - val_loss: 0.9557 - val_accuracy: 0.0000e+00\n",
      "Epoch 87/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5357 - accuracy: 0.7480 - val_loss: 0.9566 - val_accuracy: 0.0000e+00\n",
      "Epoch 88/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5364 - accuracy: 0.7717 - val_loss: 0.9534 - val_accuracy: 0.0000e+00\n",
      "Epoch 89/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5094 - accuracy: 0.8110 - val_loss: 0.9538 - val_accuracy: 0.0000e+00\n",
      "Epoch 90/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5022 - accuracy: 0.7559 - val_loss: 0.9523 - val_accuracy: 0.0000e+00\n",
      "Epoch 91/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4845 - accuracy: 0.7953 - val_loss: 0.9429 - val_accuracy: 0.0000e+00\n",
      "Epoch 92/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5077 - accuracy: 0.7559 - val_loss: 0.9491 - val_accuracy: 0.0000e+00\n",
      "Epoch 93/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.5069 - accuracy: 0.7795 - val_loss: 0.9402 - val_accuracy: 0.0000e+00\n",
      "Epoch 94/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5395 - accuracy: 0.7323 - val_loss: 0.9539 - val_accuracy: 0.0000e+00\n",
      "Epoch 95/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4895 - accuracy: 0.7795 - val_loss: 0.9519 - val_accuracy: 0.0000e+00\n",
      "Epoch 96/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5042 - accuracy: 0.7559 - val_loss: 0.9521 - val_accuracy: 0.0000e+00\n",
      "Epoch 97/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5198 - accuracy: 0.7323 - val_loss: 0.9554 - val_accuracy: 0.0000e+00\n",
      "Epoch 98/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4942 - accuracy: 0.7717 - val_loss: 0.9498 - val_accuracy: 0.0000e+00\n",
      "Epoch 99/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4856 - accuracy: 0.8031 - val_loss: 0.9514 - val_accuracy: 0.0000e+00\n",
      "Epoch 100/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4847 - accuracy: 0.7874 - val_loss: 0.9472 - val_accuracy: 0.0000e+00\n",
      "Epoch 101/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4553 - accuracy: 0.8189 - val_loss: 0.9513 - val_accuracy: 0.0000e+00\n",
      "Epoch 102/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4881 - accuracy: 0.7953 - val_loss: 0.9435 - val_accuracy: 0.0000e+00\n",
      "Epoch 103/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.4816 - accuracy: 0.7480 - val_loss: 0.9379 - val_accuracy: 0.0000e+00\n",
      "Epoch 104/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.5157 - accuracy: 0.7323 - val_loss: 0.9320 - val_accuracy: 0.0000e+00\n",
      "Epoch 105/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.5017 - accuracy: 0.7480 - val_loss: 0.9341 - val_accuracy: 0.0000e+00\n",
      "Epoch 106/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4710 - accuracy: 0.7874 - val_loss: 0.9360 - val_accuracy: 0.0000e+00\n",
      "Epoch 107/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4663 - accuracy: 0.7795 - val_loss: 0.9379 - val_accuracy: 0.0000e+00\n",
      "Epoch 108/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4755 - accuracy: 0.7717 - val_loss: 0.9411 - val_accuracy: 0.0000e+00\n",
      "Epoch 109/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4808 - accuracy: 0.7795 - val_loss: 0.9354 - val_accuracy: 0.0000e+00\n",
      "Epoch 110/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.5101 - accuracy: 0.7559 - val_loss: 0.9338 - val_accuracy: 0.0000e+00\n",
      "Epoch 111/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4875 - accuracy: 0.8110 - val_loss: 0.9287 - val_accuracy: 0.0000e+00\n",
      "Epoch 112/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4899 - accuracy: 0.7402 - val_loss: 0.9260 - val_accuracy: 0.0000e+00\n",
      "Epoch 113/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4495 - accuracy: 0.8110 - val_loss: 0.9236 - val_accuracy: 0.0000e+00\n",
      "Epoch 114/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4383 - accuracy: 0.7874 - val_loss: 0.9280 - val_accuracy: 0.0000e+00\n",
      "Epoch 115/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4697 - accuracy: 0.7874 - val_loss: 0.9259 - val_accuracy: 0.0000e+00\n",
      "Epoch 116/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4955 - accuracy: 0.7717 - val_loss: 0.9224 - val_accuracy: 0.0000e+00\n",
      "Epoch 117/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.4887 - accuracy: 0.7953 - val_loss: 0.9250 - val_accuracy: 0.0000e+00\n",
      "Epoch 118/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4961 - accuracy: 0.7559 - val_loss: 0.9277 - val_accuracy: 0.0000e+00\n",
      "Epoch 119/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4831 - accuracy: 0.7638 - val_loss: 0.9161 - val_accuracy: 0.0000e+00\n",
      "Epoch 120/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4929 - accuracy: 0.7638 - val_loss: 0.9155 - val_accuracy: 0.0000e+00\n",
      "Epoch 121/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4594 - accuracy: 0.7717 - val_loss: 0.9202 - val_accuracy: 0.0000e+00\n",
      "Epoch 122/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4346 - accuracy: 0.8031 - val_loss: 0.9117 - val_accuracy: 0.0000e+00\n",
      "Epoch 123/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4801 - accuracy: 0.7953 - val_loss: 0.9156 - val_accuracy: 0.0000e+00\n",
      "Epoch 124/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.4245 - accuracy: 0.8268 - val_loss: 0.9168 - val_accuracy: 0.0000e+00\n",
      "Epoch 125/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4777 - accuracy: 0.7559 - val_loss: 0.9189 - val_accuracy: 0.0000e+00\n",
      "Epoch 126/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4541 - accuracy: 0.7874 - val_loss: 0.9201 - val_accuracy: 0.0000e+00\n",
      "Epoch 127/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4630 - accuracy: 0.7795 - val_loss: 0.9207 - val_accuracy: 0.0000e+00\n",
      "Epoch 128/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4634 - accuracy: 0.7480 - val_loss: 0.9211 - val_accuracy: 0.0000e+00\n",
      "Epoch 129/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4492 - accuracy: 0.8110 - val_loss: 0.9177 - val_accuracy: 0.0000e+00\n",
      "Epoch 130/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4585 - accuracy: 0.7717 - val_loss: 0.9112 - val_accuracy: 0.0000e+00\n",
      "Epoch 131/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4836 - accuracy: 0.7717 - val_loss: 0.9106 - val_accuracy: 0.0000e+00\n",
      "Epoch 132/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4306 - accuracy: 0.8031 - val_loss: 0.9045 - val_accuracy: 0.0000e+00\n",
      "Epoch 133/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4260 - accuracy: 0.8504 - val_loss: 0.9090 - val_accuracy: 0.0000e+00\n",
      "Epoch 134/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4899 - accuracy: 0.7244 - val_loss: 0.9143 - val_accuracy: 0.0000e+00\n",
      "Epoch 135/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4333 - accuracy: 0.8031 - val_loss: 0.9111 - val_accuracy: 0.0000e+00\n",
      "Epoch 136/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4741 - accuracy: 0.7559 - val_loss: 0.9160 - val_accuracy: 0.0000e+00\n",
      "Epoch 137/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4139 - accuracy: 0.7953 - val_loss: 0.9161 - val_accuracy: 0.0000e+00\n",
      "Epoch 138/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4378 - accuracy: 0.8189 - val_loss: 0.9118 - val_accuracy: 0.0000e+00\n",
      "Epoch 139/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4422 - accuracy: 0.8031 - val_loss: 0.9028 - val_accuracy: 0.0000e+00\n",
      "Epoch 140/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4748 - accuracy: 0.7795 - val_loss: 0.8996 - val_accuracy: 0.0000e+00\n",
      "Epoch 141/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4486 - accuracy: 0.7559 - val_loss: 0.9042 - val_accuracy: 0.0000e+00\n",
      "Epoch 142/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4712 - accuracy: 0.7638 - val_loss: 0.9098 - val_accuracy: 0.0000e+00\n",
      "Epoch 143/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4565 - accuracy: 0.7874 - val_loss: 0.9064 - val_accuracy: 0.0000e+00\n",
      "Epoch 144/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4523 - accuracy: 0.7559 - val_loss: 0.8981 - val_accuracy: 0.0000e+00\n",
      "Epoch 145/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.4471 - accuracy: 0.7795 - val_loss: 0.9066 - val_accuracy: 0.0000e+00\n",
      "Epoch 146/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4462 - accuracy: 0.8031 - val_loss: 0.9076 - val_accuracy: 0.0000e+00\n",
      "Epoch 147/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4441 - accuracy: 0.7874 - val_loss: 0.9101 - val_accuracy: 0.0000e+00\n",
      "Epoch 148/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4568 - accuracy: 0.7795 - val_loss: 0.9119 - val_accuracy: 0.0000e+00\n",
      "Epoch 149/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4598 - accuracy: 0.7638 - val_loss: 0.9055 - val_accuracy: 0.0000e+00\n",
      "Epoch 150/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4645 - accuracy: 0.7638 - val_loss: 0.9049 - val_accuracy: 0.0000e+00\n",
      "Epoch 151/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4378 - accuracy: 0.8031 - val_loss: 0.8999 - val_accuracy: 0.0000e+00\n",
      "Epoch 152/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4549 - accuracy: 0.7717 - val_loss: 0.8998 - val_accuracy: 0.0000e+00\n",
      "Epoch 153/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4361 - accuracy: 0.8031 - val_loss: 0.9027 - val_accuracy: 0.0000e+00\n",
      "Epoch 154/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4174 - accuracy: 0.8031 - val_loss: 0.9023 - val_accuracy: 0.0000e+00\n",
      "Epoch 155/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4280 - accuracy: 0.7795 - val_loss: 0.8995 - val_accuracy: 0.0000e+00\n",
      "Epoch 156/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4273 - accuracy: 0.7638 - val_loss: 0.9002 - val_accuracy: 0.0000e+00\n",
      "Epoch 157/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4540 - accuracy: 0.7717 - val_loss: 0.9048 - val_accuracy: 0.0000e+00\n",
      "Epoch 158/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4185 - accuracy: 0.8110 - val_loss: 0.9018 - val_accuracy: 0.0000e+00\n",
      "Epoch 159/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4493 - accuracy: 0.7717 - val_loss: 0.8954 - val_accuracy: 0.0000e+00\n",
      "Epoch 160/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4374 - accuracy: 0.8031 - val_loss: 0.8925 - val_accuracy: 0.0000e+00\n",
      "Epoch 161/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4323 - accuracy: 0.7638 - val_loss: 0.8941 - val_accuracy: 0.0000e+00\n",
      "Epoch 162/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4351 - accuracy: 0.8031 - val_loss: 0.8873 - val_accuracy: 0.0000e+00\n",
      "Epoch 163/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4110 - accuracy: 0.8268 - val_loss: 0.8862 - val_accuracy: 0.0000e+00\n",
      "Epoch 164/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4085 - accuracy: 0.8110 - val_loss: 0.8855 - val_accuracy: 0.0000e+00\n",
      "Epoch 165/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4518 - accuracy: 0.7874 - val_loss: 0.8915 - val_accuracy: 0.0000e+00\n",
      "Epoch 166/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4403 - accuracy: 0.7874 - val_loss: 0.8917 - val_accuracy: 0.0000e+00\n",
      "Epoch 167/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4304 - accuracy: 0.7874 - val_loss: 0.8887 - val_accuracy: 0.0000e+00\n",
      "Epoch 168/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4141 - accuracy: 0.7953 - val_loss: 0.8835 - val_accuracy: 0.0000e+00\n",
      "Epoch 169/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4052 - accuracy: 0.7953 - val_loss: 0.8822 - val_accuracy: 0.0000e+00\n",
      "Epoch 170/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4150 - accuracy: 0.8189 - val_loss: 0.8820 - val_accuracy: 0.0000e+00\n",
      "Epoch 171/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4160 - accuracy: 0.8346 - val_loss: 0.8817 - val_accuracy: 0.0000e+00\n",
      "Epoch 172/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4311 - accuracy: 0.7638 - val_loss: 0.8781 - val_accuracy: 0.0000e+00\n",
      "Epoch 173/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4081 - accuracy: 0.8189 - val_loss: 0.8810 - val_accuracy: 0.0000e+00\n",
      "Epoch 174/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4342 - accuracy: 0.7795 - val_loss: 0.8805 - val_accuracy: 0.0000e+00\n",
      "Epoch 175/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4139 - accuracy: 0.8031 - val_loss: 0.8796 - val_accuracy: 0.0000e+00\n",
      "Epoch 176/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4038 - accuracy: 0.7795 - val_loss: 0.8793 - val_accuracy: 0.0000e+00\n",
      "Epoch 177/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.4347 - accuracy: 0.7402 - val_loss: 0.8785 - val_accuracy: 0.0000e+00\n",
      "Epoch 178/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4309 - accuracy: 0.7953 - val_loss: 0.8767 - val_accuracy: 0.0000e+00\n",
      "Epoch 179/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4298 - accuracy: 0.8031 - val_loss: 0.8769 - val_accuracy: 0.0000e+00\n",
      "Epoch 180/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3920 - accuracy: 0.8110 - val_loss: 0.8743 - val_accuracy: 0.0000e+00\n",
      "Epoch 181/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.4199 - accuracy: 0.7953 - val_loss: 0.8729 - val_accuracy: 0.0000e+00\n",
      "Epoch 182/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3869 - accuracy: 0.8425 - val_loss: 0.8766 - val_accuracy: 0.0000e+00\n",
      "Epoch 183/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4145 - accuracy: 0.8110 - val_loss: 0.8737 - val_accuracy: 0.0000e+00\n",
      "Epoch 184/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4171 - accuracy: 0.7874 - val_loss: 0.8713 - val_accuracy: 0.0000e+00\n",
      "Epoch 185/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4294 - accuracy: 0.8031 - val_loss: 0.8700 - val_accuracy: 0.0000e+00\n",
      "Epoch 186/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4020 - accuracy: 0.7795 - val_loss: 0.8717 - val_accuracy: 0.0000e+00\n",
      "Epoch 187/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4131 - accuracy: 0.7953 - val_loss: 0.8704 - val_accuracy: 0.0000e+00\n",
      "Epoch 188/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4309 - accuracy: 0.8268 - val_loss: 0.8657 - val_accuracy: 0.0000e+00\n",
      "Epoch 189/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4089 - accuracy: 0.8031 - val_loss: 0.8644 - val_accuracy: 0.0000e+00\n",
      "Epoch 190/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4590 - accuracy: 0.7638 - val_loss: 0.8646 - val_accuracy: 0.0000e+00\n",
      "Epoch 191/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4402 - accuracy: 0.7795 - val_loss: 0.8639 - val_accuracy: 0.0000e+00\n",
      "Epoch 192/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4111 - accuracy: 0.7795 - val_loss: 0.8637 - val_accuracy: 0.0000e+00\n",
      "Epoch 193/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4747 - accuracy: 0.7402 - val_loss: 0.8655 - val_accuracy: 0.0000e+00\n",
      "Epoch 194/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.4396 - accuracy: 0.7638 - val_loss: 0.8687 - val_accuracy: 0.0000e+00\n",
      "Epoch 195/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4146 - accuracy: 0.8110 - val_loss: 0.8669 - val_accuracy: 0.0000e+00\n",
      "Epoch 196/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4487 - accuracy: 0.7795 - val_loss: 0.8699 - val_accuracy: 0.0000e+00\n",
      "Epoch 197/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3998 - accuracy: 0.8031 - val_loss: 0.8641 - val_accuracy: 0.0000e+00\n",
      "Epoch 198/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4243 - accuracy: 0.7953 - val_loss: 0.8669 - val_accuracy: 0.0000e+00\n",
      "Epoch 199/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4402 - accuracy: 0.7638 - val_loss: 0.8692 - val_accuracy: 0.0000e+00\n",
      "Epoch 200/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4327 - accuracy: 0.8110 - val_loss: 0.8716 - val_accuracy: 0.0000e+00\n",
      "Epoch 201/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3816 - accuracy: 0.8110 - val_loss: 0.8673 - val_accuracy: 0.0000e+00\n",
      "Epoch 202/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3922 - accuracy: 0.8268 - val_loss: 0.8616 - val_accuracy: 0.0000e+00\n",
      "Epoch 203/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4321 - accuracy: 0.7717 - val_loss: 0.8598 - val_accuracy: 0.0000e+00\n",
      "Epoch 204/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4075 - accuracy: 0.8031 - val_loss: 0.8623 - val_accuracy: 0.0000e+00\n",
      "Epoch 205/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4352 - accuracy: 0.7795 - val_loss: 0.8643 - val_accuracy: 0.0000e+00\n",
      "Epoch 206/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3763 - accuracy: 0.8268 - val_loss: 0.8645 - val_accuracy: 0.0000e+00\n",
      "Epoch 207/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4065 - accuracy: 0.8110 - val_loss: 0.8624 - val_accuracy: 0.0000e+00\n",
      "Epoch 208/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3847 - accuracy: 0.8031 - val_loss: 0.8661 - val_accuracy: 0.0000e+00\n",
      "Epoch 209/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3994 - accuracy: 0.8189 - val_loss: 0.8698 - val_accuracy: 0.0000e+00\n",
      "Epoch 210/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3905 - accuracy: 0.8346 - val_loss: 0.8651 - val_accuracy: 0.0000e+00\n",
      "Epoch 211/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4239 - accuracy: 0.7874 - val_loss: 0.8687 - val_accuracy: 0.0000e+00\n",
      "Epoch 212/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3897 - accuracy: 0.8189 - val_loss: 0.8715 - val_accuracy: 0.0000e+00\n",
      "Epoch 213/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4192 - accuracy: 0.8031 - val_loss: 0.8715 - val_accuracy: 0.0000e+00\n",
      "Epoch 214/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4167 - accuracy: 0.7795 - val_loss: 0.8691 - val_accuracy: 0.0000e+00\n",
      "Epoch 215/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4406 - accuracy: 0.7717 - val_loss: 0.8652 - val_accuracy: 0.0000e+00\n",
      "Epoch 216/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.4158 - accuracy: 0.7953 - val_loss: 0.8602 - val_accuracy: 0.0000e+00\n",
      "Epoch 217/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3976 - accuracy: 0.7717 - val_loss: 0.8613 - val_accuracy: 0.0000e+00\n",
      "Epoch 218/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3796 - accuracy: 0.8268 - val_loss: 0.8607 - val_accuracy: 0.0000e+00\n",
      "Epoch 219/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4449 - accuracy: 0.7559 - val_loss: 0.8562 - val_accuracy: 0.0000e+00\n",
      "Epoch 220/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4173 - accuracy: 0.8031 - val_loss: 0.8525 - val_accuracy: 0.0000e+00\n",
      "Epoch 221/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3980 - accuracy: 0.8110 - val_loss: 0.8486 - val_accuracy: 0.0000e+00\n",
      "Epoch 222/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4236 - accuracy: 0.8110 - val_loss: 0.8442 - val_accuracy: 0.0000e+00\n",
      "Epoch 223/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3850 - accuracy: 0.8268 - val_loss: 0.8423 - val_accuracy: 0.0000e+00\n",
      "Epoch 224/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4131 - accuracy: 0.7795 - val_loss: 0.8493 - val_accuracy: 0.0000e+00\n",
      "Epoch 225/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3845 - accuracy: 0.8110 - val_loss: 0.8491 - val_accuracy: 0.0000e+00\n",
      "Epoch 226/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3853 - accuracy: 0.8189 - val_loss: 0.8480 - val_accuracy: 0.0000e+00\n",
      "Epoch 227/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4082 - accuracy: 0.8189 - val_loss: 0.8441 - val_accuracy: 0.0000e+00\n",
      "Epoch 228/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4109 - accuracy: 0.7874 - val_loss: 0.8473 - val_accuracy: 0.0000e+00\n",
      "Epoch 229/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4247 - accuracy: 0.8110 - val_loss: 0.8411 - val_accuracy: 0.0000e+00\n",
      "Epoch 230/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3948 - accuracy: 0.7795 - val_loss: 0.8418 - val_accuracy: 0.0000e+00\n",
      "Epoch 231/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3772 - accuracy: 0.8504 - val_loss: 0.8417 - val_accuracy: 0.0000e+00\n",
      "Epoch 232/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4232 - accuracy: 0.7717 - val_loss: 0.8425 - val_accuracy: 0.0000e+00\n",
      "Epoch 233/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4255 - accuracy: 0.7559 - val_loss: 0.8434 - val_accuracy: 0.0000e+00\n",
      "Epoch 234/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3822 - accuracy: 0.8346 - val_loss: 0.8495 - val_accuracy: 0.0000e+00\n",
      "Epoch 235/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3884 - accuracy: 0.8031 - val_loss: 0.8537 - val_accuracy: 0.0000e+00\n",
      "Epoch 236/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3875 - accuracy: 0.8425 - val_loss: 0.8538 - val_accuracy: 0.0000e+00\n",
      "Epoch 237/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3749 - accuracy: 0.8031 - val_loss: 0.8545 - val_accuracy: 0.0000e+00\n",
      "Epoch 238/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4003 - accuracy: 0.8031 - val_loss: 0.8526 - val_accuracy: 0.0000e+00\n",
      "Epoch 239/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3717 - accuracy: 0.8583 - val_loss: 0.8541 - val_accuracy: 0.0000e+00\n",
      "Epoch 240/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4061 - accuracy: 0.8189 - val_loss: 0.8490 - val_accuracy: 0.0000e+00\n",
      "Epoch 241/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3954 - accuracy: 0.8110 - val_loss: 0.8493 - val_accuracy: 0.0000e+00\n",
      "Epoch 242/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3801 - accuracy: 0.7874 - val_loss: 0.8515 - val_accuracy: 0.0000e+00\n",
      "Epoch 243/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4096 - accuracy: 0.7638 - val_loss: 0.8464 - val_accuracy: 0.0000e+00\n",
      "Epoch 244/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4252 - accuracy: 0.7874 - val_loss: 0.8417 - val_accuracy: 0.0000e+00\n",
      "Epoch 245/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3942 - accuracy: 0.8189 - val_loss: 0.8456 - val_accuracy: 0.0000e+00\n",
      "Epoch 246/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3905 - accuracy: 0.8031 - val_loss: 0.8498 - val_accuracy: 0.0000e+00\n",
      "Epoch 247/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4189 - accuracy: 0.7874 - val_loss: 0.8557 - val_accuracy: 0.0000e+00\n",
      "Epoch 248/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4061 - accuracy: 0.7638 - val_loss: 0.8562 - val_accuracy: 0.0000e+00\n",
      "Epoch 249/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4125 - accuracy: 0.8189 - val_loss: 0.8567 - val_accuracy: 0.0000e+00\n",
      "Epoch 250/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4086 - accuracy: 0.8110 - val_loss: 0.8479 - val_accuracy: 0.0000e+00\n",
      "Epoch 251/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3858 - accuracy: 0.8268 - val_loss: 0.8450 - val_accuracy: 0.0000e+00\n",
      "Epoch 252/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4094 - accuracy: 0.7874 - val_loss: 0.8432 - val_accuracy: 0.0000e+00\n",
      "Epoch 253/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 0.3892 - accuracy: 0.7874 - val_loss: 0.8431 - val_accuracy: 0.0000e+00\n",
      "Epoch 254/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3944 - accuracy: 0.8189 - val_loss: 0.8360 - val_accuracy: 0.0000e+00\n",
      "Epoch 255/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3744 - accuracy: 0.8346 - val_loss: 0.8435 - val_accuracy: 0.0000e+00\n",
      "Epoch 256/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3770 - accuracy: 0.8268 - val_loss: 0.8444 - val_accuracy: 0.0000e+00\n",
      "Epoch 257/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3825 - accuracy: 0.8031 - val_loss: 0.8374 - val_accuracy: 0.0000e+00\n",
      "Epoch 258/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3883 - accuracy: 0.8031 - val_loss: 0.8296 - val_accuracy: 0.0000e+00\n",
      "Epoch 259/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4008 - accuracy: 0.8110 - val_loss: 0.8317 - val_accuracy: 0.0000e+00\n",
      "Epoch 260/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3870 - accuracy: 0.7874 - val_loss: 0.8283 - val_accuracy: 0.0000e+00\n",
      "Epoch 261/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.4109 - accuracy: 0.8189 - val_loss: 0.8306 - val_accuracy: 0.0000e+00\n",
      "Epoch 262/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3929 - accuracy: 0.7795 - val_loss: 0.8260 - val_accuracy: 0.0000e+00\n",
      "Epoch 263/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3985 - accuracy: 0.8189 - val_loss: 0.8225 - val_accuracy: 0.0000e+00\n",
      "Epoch 264/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3941 - accuracy: 0.7953 - val_loss: 0.8147 - val_accuracy: 0.0000e+00\n",
      "Epoch 265/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4059 - accuracy: 0.7953 - val_loss: 0.8142 - val_accuracy: 0.0000e+00\n",
      "Epoch 266/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3730 - accuracy: 0.7953 - val_loss: 0.8118 - val_accuracy: 0.0000e+00\n",
      "Epoch 267/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3779 - accuracy: 0.8268 - val_loss: 0.8126 - val_accuracy: 0.0000e+00\n",
      "Epoch 268/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.4175 - accuracy: 0.8031 - val_loss: 0.8157 - val_accuracy: 0.0000e+00\n",
      "Epoch 269/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3720 - accuracy: 0.8504 - val_loss: 0.8164 - val_accuracy: 0.0000e+00\n",
      "Epoch 270/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3873 - accuracy: 0.7953 - val_loss: 0.8093 - val_accuracy: 0.0000e+00\n",
      "Epoch 271/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3844 - accuracy: 0.7953 - val_loss: 0.8116 - val_accuracy: 0.0000e+00\n",
      "Epoch 272/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3608 - accuracy: 0.8346 - val_loss: 0.8123 - val_accuracy: 0.0000e+00\n",
      "Epoch 273/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3997 - accuracy: 0.7874 - val_loss: 0.8103 - val_accuracy: 0.0000e+00\n",
      "Epoch 274/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3782 - accuracy: 0.7953 - val_loss: 0.8013 - val_accuracy: 0.0435\n",
      "Epoch 275/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3746 - accuracy: 0.8268 - val_loss: 0.7972 - val_accuracy: 0.0435\n",
      "Epoch 276/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3802 - accuracy: 0.8031 - val_loss: 0.7970 - val_accuracy: 0.0435\n",
      "Epoch 277/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3772 - accuracy: 0.7874 - val_loss: 0.7967 - val_accuracy: 0.0435\n",
      "Epoch 278/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3631 - accuracy: 0.8425 - val_loss: 0.7969 - val_accuracy: 0.0435\n",
      "Epoch 279/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3799 - accuracy: 0.7953 - val_loss: 0.7944 - val_accuracy: 0.0870\n",
      "Epoch 280/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3581 - accuracy: 0.8504 - val_loss: 0.7943 - val_accuracy: 0.0870\n",
      "Epoch 281/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4002 - accuracy: 0.7953 - val_loss: 0.7946 - val_accuracy: 0.0870\n",
      "Epoch 282/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3571 - accuracy: 0.8661 - val_loss: 0.7952 - val_accuracy: 0.0870\n",
      "Epoch 283/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3652 - accuracy: 0.8346 - val_loss: 0.7956 - val_accuracy: 0.0870\n",
      "Epoch 284/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3801 - accuracy: 0.7874 - val_loss: 0.7937 - val_accuracy: 0.0870\n",
      "Epoch 285/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3863 - accuracy: 0.8031 - val_loss: 0.7976 - val_accuracy: 0.0870\n",
      "Epoch 286/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4111 - accuracy: 0.7717 - val_loss: 0.8081 - val_accuracy: 0.0435\n",
      "Epoch 287/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.4141 - accuracy: 0.8031 - val_loss: 0.8044 - val_accuracy: 0.0435\n",
      "Epoch 288/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3741 - accuracy: 0.8031 - val_loss: 0.8022 - val_accuracy: 0.0435\n",
      "Epoch 289/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3661 - accuracy: 0.8661 - val_loss: 0.7986 - val_accuracy: 0.0870\n",
      "Epoch 290/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3531 - accuracy: 0.8346 - val_loss: 0.7962 - val_accuracy: 0.0870\n",
      "Epoch 291/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4061 - accuracy: 0.7795 - val_loss: 0.8013 - val_accuracy: 0.0870\n",
      "Epoch 292/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3723 - accuracy: 0.8268 - val_loss: 0.7989 - val_accuracy: 0.0870\n",
      "Epoch 293/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3732 - accuracy: 0.8268 - val_loss: 0.8005 - val_accuracy: 0.0870\n",
      "Epoch 294/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3681 - accuracy: 0.8268 - val_loss: 0.8005 - val_accuracy: 0.0870\n",
      "Epoch 295/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3778 - accuracy: 0.8268 - val_loss: 0.8004 - val_accuracy: 0.0870\n",
      "Epoch 296/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3723 - accuracy: 0.8346 - val_loss: 0.7970 - val_accuracy: 0.0870\n",
      "Epoch 297/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3496 - accuracy: 0.8425 - val_loss: 0.7933 - val_accuracy: 0.0870\n",
      "Epoch 298/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3882 - accuracy: 0.7795 - val_loss: 0.7938 - val_accuracy: 0.0870\n",
      "Epoch 299/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3487 - accuracy: 0.8031 - val_loss: 0.7900 - val_accuracy: 0.0870\n",
      "Epoch 300/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3932 - accuracy: 0.7953 - val_loss: 0.7875 - val_accuracy: 0.0870\n",
      "Epoch 301/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3980 - accuracy: 0.8189 - val_loss: 0.7915 - val_accuracy: 0.0870\n",
      "Epoch 302/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3556 - accuracy: 0.8268 - val_loss: 0.7890 - val_accuracy: 0.0870\n",
      "Epoch 303/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3768 - accuracy: 0.8110 - val_loss: 0.7897 - val_accuracy: 0.0870\n",
      "Epoch 304/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3844 - accuracy: 0.8346 - val_loss: 0.7846 - val_accuracy: 0.0870\n",
      "Epoch 305/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 0.3663 - accuracy: 0.8189 - val_loss: 0.7869 - val_accuracy: 0.0870\n",
      "Epoch 306/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3756 - accuracy: 0.8268 - val_loss: 0.7853 - val_accuracy: 0.0870\n",
      "Epoch 307/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3650 - accuracy: 0.8189 - val_loss: 0.7870 - val_accuracy: 0.0870\n",
      "Epoch 308/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3902 - accuracy: 0.8425 - val_loss: 0.7884 - val_accuracy: 0.0870\n",
      "Epoch 309/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3400 - accuracy: 0.8425 - val_loss: 0.7850 - val_accuracy: 0.0870\n",
      "Epoch 310/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3786 - accuracy: 0.7874 - val_loss: 0.7863 - val_accuracy: 0.0870\n",
      "Epoch 311/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3646 - accuracy: 0.8504 - val_loss: 0.7848 - val_accuracy: 0.0870\n",
      "Epoch 312/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3538 - accuracy: 0.8583 - val_loss: 0.7851 - val_accuracy: 0.0870\n",
      "Epoch 313/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3839 - accuracy: 0.7953 - val_loss: 0.7798 - val_accuracy: 0.0870\n",
      "Epoch 314/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3817 - accuracy: 0.8268 - val_loss: 0.7772 - val_accuracy: 0.1304\n",
      "Epoch 315/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3540 - accuracy: 0.8110 - val_loss: 0.7727 - val_accuracy: 0.1304\n",
      "Epoch 316/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3848 - accuracy: 0.7874 - val_loss: 0.7714 - val_accuracy: 0.1739\n",
      "Epoch 317/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3473 - accuracy: 0.8346 - val_loss: 0.7717 - val_accuracy: 0.1739\n",
      "Epoch 318/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3763 - accuracy: 0.8346 - val_loss: 0.7740 - val_accuracy: 0.1304\n",
      "Epoch 319/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3797 - accuracy: 0.8110 - val_loss: 0.7727 - val_accuracy: 0.1739\n",
      "Epoch 320/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3682 - accuracy: 0.8189 - val_loss: 0.7681 - val_accuracy: 0.2174\n",
      "Epoch 321/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3566 - accuracy: 0.8189 - val_loss: 0.7712 - val_accuracy: 0.1739\n",
      "Epoch 322/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3681 - accuracy: 0.8189 - val_loss: 0.7646 - val_accuracy: 0.3043\n",
      "Epoch 323/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3730 - accuracy: 0.8189 - val_loss: 0.7686 - val_accuracy: 0.2174\n",
      "Epoch 324/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3739 - accuracy: 0.8110 - val_loss: 0.7651 - val_accuracy: 0.3043\n",
      "Epoch 325/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3663 - accuracy: 0.8661 - val_loss: 0.7682 - val_accuracy: 0.2174\n",
      "Epoch 326/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3484 - accuracy: 0.8504 - val_loss: 0.7715 - val_accuracy: 0.2174\n",
      "Epoch 327/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3792 - accuracy: 0.8346 - val_loss: 0.7661 - val_accuracy: 0.3478\n",
      "Epoch 328/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3543 - accuracy: 0.8268 - val_loss: 0.7650 - val_accuracy: 0.3478\n",
      "Epoch 329/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3291 - accuracy: 0.8583 - val_loss: 0.7616 - val_accuracy: 0.3478\n",
      "Epoch 330/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3718 - accuracy: 0.8268 - val_loss: 0.7590 - val_accuracy: 0.3913\n",
      "Epoch 331/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 0.3527 - accuracy: 0.8268 - val_loss: 0.7549 - val_accuracy: 0.4348\n",
      "Epoch 332/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3859 - accuracy: 0.8268 - val_loss: 0.7548 - val_accuracy: 0.4348\n",
      "Epoch 333/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3657 - accuracy: 0.8346 - val_loss: 0.7535 - val_accuracy: 0.4348\n",
      "Epoch 334/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 0.3529 - accuracy: 0.8268 - val_loss: 0.7493 - val_accuracy: 0.4348\n",
      "Epoch 335/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3375 - accuracy: 0.8976 - val_loss: 0.7472 - val_accuracy: 0.4783\n",
      "Epoch 336/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3753 - accuracy: 0.8268 - val_loss: 0.7490 - val_accuracy: 0.4348\n",
      "Epoch 337/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3362 - accuracy: 0.8504 - val_loss: 0.7442 - val_accuracy: 0.4783\n",
      "Epoch 338/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3555 - accuracy: 0.8425 - val_loss: 0.7419 - val_accuracy: 0.4783\n",
      "Epoch 339/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3430 - accuracy: 0.8425 - val_loss: 0.7355 - val_accuracy: 0.4783\n",
      "Epoch 340/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3567 - accuracy: 0.8504 - val_loss: 0.7339 - val_accuracy: 0.5217\n",
      "Epoch 341/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3240 - accuracy: 0.8819 - val_loss: 0.7368 - val_accuracy: 0.4783\n",
      "Epoch 342/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3704 - accuracy: 0.8268 - val_loss: 0.7333 - val_accuracy: 0.5217\n",
      "Epoch 343/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3728 - accuracy: 0.7953 - val_loss: 0.7407 - val_accuracy: 0.4783\n",
      "Epoch 344/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3517 - accuracy: 0.8346 - val_loss: 0.7399 - val_accuracy: 0.4783\n",
      "Epoch 345/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3788 - accuracy: 0.8031 - val_loss: 0.7404 - val_accuracy: 0.4783\n",
      "Epoch 346/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3489 - accuracy: 0.8189 - val_loss: 0.7418 - val_accuracy: 0.4783\n",
      "Epoch 347/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3910 - accuracy: 0.7874 - val_loss: 0.7413 - val_accuracy: 0.4783\n",
      "Epoch 348/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 0.3763 - accuracy: 0.8110 - val_loss: 0.7429 - val_accuracy: 0.4783\n",
      "Epoch 349/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3527 - accuracy: 0.8110 - val_loss: 0.7384 - val_accuracy: 0.4783\n",
      "Epoch 350/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 0.3273 - accuracy: 0.8504 - val_loss: 0.7421 - val_accuracy: 0.4783\n",
      "Epoch 351/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3670 - accuracy: 0.8189 - val_loss: 0.7380 - val_accuracy: 0.4783\n",
      "Epoch 352/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3358 - accuracy: 0.8661 - val_loss: 0.7343 - val_accuracy: 0.5217\n",
      "Epoch 353/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 0.3571 - accuracy: 0.8189 - val_loss: 0.7322 - val_accuracy: 0.5217\n",
      "Epoch 354/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3446 - accuracy: 0.8504 - val_loss: 0.7368 - val_accuracy: 0.5217\n",
      "Epoch 355/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3500 - accuracy: 0.8189 - val_loss: 0.7339 - val_accuracy: 0.5217\n",
      "Epoch 356/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3388 - accuracy: 0.8346 - val_loss: 0.7311 - val_accuracy: 0.5217\n",
      "Epoch 357/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3641 - accuracy: 0.8268 - val_loss: 0.7333 - val_accuracy: 0.5217\n",
      "Epoch 358/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3403 - accuracy: 0.8583 - val_loss: 0.7363 - val_accuracy: 0.5217\n",
      "Epoch 359/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 0.3373 - accuracy: 0.8110 - val_loss: 0.7375 - val_accuracy: 0.4783\n",
      "Epoch 360/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3308 - accuracy: 0.8425 - val_loss: 0.7340 - val_accuracy: 0.5217\n",
      "Epoch 361/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3323 - accuracy: 0.8583 - val_loss: 0.7283 - val_accuracy: 0.5217\n",
      "Epoch 362/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3431 - accuracy: 0.8346 - val_loss: 0.7263 - val_accuracy: 0.5217\n",
      "Epoch 363/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3420 - accuracy: 0.8583 - val_loss: 0.7318 - val_accuracy: 0.5217\n",
      "Epoch 364/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3371 - accuracy: 0.8740 - val_loss: 0.7287 - val_accuracy: 0.5217\n",
      "Epoch 365/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3516 - accuracy: 0.8740 - val_loss: 0.7267 - val_accuracy: 0.5217\n",
      "Epoch 366/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3378 - accuracy: 0.8504 - val_loss: 0.7185 - val_accuracy: 0.5217\n",
      "Epoch 367/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3177 - accuracy: 0.8740 - val_loss: 0.7212 - val_accuracy: 0.5217\n",
      "Epoch 368/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3319 - accuracy: 0.8189 - val_loss: 0.7143 - val_accuracy: 0.5217\n",
      "Epoch 369/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3513 - accuracy: 0.8819 - val_loss: 0.7166 - val_accuracy: 0.5217\n",
      "Epoch 370/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 0.3752 - accuracy: 0.8031 - val_loss: 0.7142 - val_accuracy: 0.5217\n",
      "Epoch 371/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3483 - accuracy: 0.8268 - val_loss: 0.7090 - val_accuracy: 0.5217\n",
      "Epoch 372/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3305 - accuracy: 0.8661 - val_loss: 0.7016 - val_accuracy: 0.5652\n",
      "Epoch 373/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3374 - accuracy: 0.8268 - val_loss: 0.7020 - val_accuracy: 0.5652\n",
      "Epoch 374/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3472 - accuracy: 0.8740 - val_loss: 0.7065 - val_accuracy: 0.5217\n",
      "Epoch 375/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3337 - accuracy: 0.8583 - val_loss: 0.7092 - val_accuracy: 0.5217\n",
      "Epoch 376/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3643 - accuracy: 0.8346 - val_loss: 0.7119 - val_accuracy: 0.5217\n",
      "Epoch 377/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3317 - accuracy: 0.8583 - val_loss: 0.7095 - val_accuracy: 0.5217\n",
      "Epoch 378/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.4067 - accuracy: 0.8031 - val_loss: 0.7060 - val_accuracy: 0.5217\n",
      "Epoch 379/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 0.3757 - accuracy: 0.8425 - val_loss: 0.7030 - val_accuracy: 0.5652\n",
      "Epoch 380/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3414 - accuracy: 0.8425 - val_loss: 0.6999 - val_accuracy: 0.5652\n",
      "Epoch 381/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3530 - accuracy: 0.8425 - val_loss: 0.6981 - val_accuracy: 0.5652\n",
      "Epoch 382/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3560 - accuracy: 0.8110 - val_loss: 0.6971 - val_accuracy: 0.6087\n",
      "Epoch 383/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3418 - accuracy: 0.8504 - val_loss: 0.6998 - val_accuracy: 0.5652\n",
      "Epoch 384/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3481 - accuracy: 0.8425 - val_loss: 0.6999 - val_accuracy: 0.5652\n",
      "Epoch 385/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3538 - accuracy: 0.8268 - val_loss: 0.6962 - val_accuracy: 0.6087\n",
      "Epoch 386/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3244 - accuracy: 0.8898 - val_loss: 0.6979 - val_accuracy: 0.6087\n",
      "Epoch 387/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3218 - accuracy: 0.8504 - val_loss: 0.6977 - val_accuracy: 0.6087\n",
      "Epoch 388/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3679 - accuracy: 0.8031 - val_loss: 0.6987 - val_accuracy: 0.6087\n",
      "Epoch 389/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3355 - accuracy: 0.8740 - val_loss: 0.6963 - val_accuracy: 0.6087\n",
      "Epoch 390/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3499 - accuracy: 0.8346 - val_loss: 0.6888 - val_accuracy: 0.6087\n",
      "Epoch 391/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3536 - accuracy: 0.8346 - val_loss: 0.6867 - val_accuracy: 0.6087\n",
      "Epoch 392/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3119 - accuracy: 0.8583 - val_loss: 0.6809 - val_accuracy: 0.6087\n",
      "Epoch 393/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3356 - accuracy: 0.8583 - val_loss: 0.6728 - val_accuracy: 0.6087\n",
      "Epoch 394/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3024 - accuracy: 0.8583 - val_loss: 0.6698 - val_accuracy: 0.6957\n",
      "Epoch 395/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3484 - accuracy: 0.8661 - val_loss: 0.6759 - val_accuracy: 0.6087\n",
      "Epoch 396/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3516 - accuracy: 0.8425 - val_loss: 0.6722 - val_accuracy: 0.6522\n",
      "Epoch 397/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3065 - accuracy: 0.8661 - val_loss: 0.6708 - val_accuracy: 0.6522\n",
      "Epoch 398/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3533 - accuracy: 0.8425 - val_loss: 0.6695 - val_accuracy: 0.6957\n",
      "Epoch 399/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3562 - accuracy: 0.8189 - val_loss: 0.6677 - val_accuracy: 0.6957\n",
      "Epoch 400/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3544 - accuracy: 0.8504 - val_loss: 0.6651 - val_accuracy: 0.6957\n",
      "Epoch 401/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3394 - accuracy: 0.8661 - val_loss: 0.6674 - val_accuracy: 0.6957\n",
      "Epoch 402/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3594 - accuracy: 0.8425 - val_loss: 0.6706 - val_accuracy: 0.6522\n",
      "Epoch 403/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3234 - accuracy: 0.8661 - val_loss: 0.6719 - val_accuracy: 0.6522\n",
      "Epoch 404/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3101 - accuracy: 0.8898 - val_loss: 0.6701 - val_accuracy: 0.6522\n",
      "Epoch 405/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3619 - accuracy: 0.8031 - val_loss: 0.6713 - val_accuracy: 0.6522\n",
      "Epoch 406/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3050 - accuracy: 0.8819 - val_loss: 0.6744 - val_accuracy: 0.6522\n",
      "Epoch 407/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3278 - accuracy: 0.8740 - val_loss: 0.6751 - val_accuracy: 0.6087\n",
      "Epoch 408/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3182 - accuracy: 0.9055 - val_loss: 0.6714 - val_accuracy: 0.6522\n",
      "Epoch 409/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3249 - accuracy: 0.8661 - val_loss: 0.6703 - val_accuracy: 0.6522\n",
      "Epoch 410/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3649 - accuracy: 0.8031 - val_loss: 0.6743 - val_accuracy: 0.6522\n",
      "Epoch 411/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3320 - accuracy: 0.8898 - val_loss: 0.6778 - val_accuracy: 0.6087\n",
      "Epoch 412/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3435 - accuracy: 0.8504 - val_loss: 0.6788 - val_accuracy: 0.6087\n",
      "Epoch 413/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3334 - accuracy: 0.8583 - val_loss: 0.6753 - val_accuracy: 0.6522\n",
      "Epoch 414/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 0.3432 - accuracy: 0.8346 - val_loss: 0.6766 - val_accuracy: 0.6087\n",
      "Epoch 415/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3787 - accuracy: 0.8583 - val_loss: 0.6742 - val_accuracy: 0.6522\n",
      "Epoch 416/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3479 - accuracy: 0.8110 - val_loss: 0.6733 - val_accuracy: 0.6522\n",
      "Epoch 417/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3133 - accuracy: 0.8504 - val_loss: 0.6688 - val_accuracy: 0.6522\n",
      "Epoch 418/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3173 - accuracy: 0.8819 - val_loss: 0.6643 - val_accuracy: 0.6957\n",
      "Epoch 419/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3334 - accuracy: 0.8583 - val_loss: 0.6600 - val_accuracy: 0.6957\n",
      "Epoch 420/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2910 - accuracy: 0.8819 - val_loss: 0.6570 - val_accuracy: 0.6957\n",
      "Epoch 421/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3210 - accuracy: 0.8740 - val_loss: 0.6585 - val_accuracy: 0.6957\n",
      "Epoch 422/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 0.3218 - accuracy: 0.8268 - val_loss: 0.6637 - val_accuracy: 0.6957\n",
      "Epoch 423/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3315 - accuracy: 0.8583 - val_loss: 0.6636 - val_accuracy: 0.6957\n",
      "Epoch 424/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3176 - accuracy: 0.8661 - val_loss: 0.6598 - val_accuracy: 0.6957\n",
      "Epoch 425/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3188 - accuracy: 0.8898 - val_loss: 0.6593 - val_accuracy: 0.6957\n",
      "Epoch 426/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3072 - accuracy: 0.8740 - val_loss: 0.6627 - val_accuracy: 0.6957\n",
      "Epoch 427/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3329 - accuracy: 0.8661 - val_loss: 0.6607 - val_accuracy: 0.6957\n",
      "Epoch 428/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3171 - accuracy: 0.8504 - val_loss: 0.6564 - val_accuracy: 0.6957\n",
      "Epoch 429/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3024 - accuracy: 0.8819 - val_loss: 0.6606 - val_accuracy: 0.6957\n",
      "Epoch 430/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3526 - accuracy: 0.8110 - val_loss: 0.6594 - val_accuracy: 0.6957\n",
      "Epoch 431/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3561 - accuracy: 0.8110 - val_loss: 0.6565 - val_accuracy: 0.6957\n",
      "Epoch 432/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3317 - accuracy: 0.8583 - val_loss: 0.6552 - val_accuracy: 0.6957\n",
      "Epoch 433/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3127 - accuracy: 0.8661 - val_loss: 0.6520 - val_accuracy: 0.6957\n",
      "Epoch 434/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3346 - accuracy: 0.8268 - val_loss: 0.6534 - val_accuracy: 0.6957\n",
      "Epoch 435/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3825 - accuracy: 0.7874 - val_loss: 0.6481 - val_accuracy: 0.6957\n",
      "Epoch 436/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3060 - accuracy: 0.8819 - val_loss: 0.6462 - val_accuracy: 0.6957\n",
      "Epoch 437/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 0.3321 - accuracy: 0.8346 - val_loss: 0.6488 - val_accuracy: 0.6957\n",
      "Epoch 438/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3409 - accuracy: 0.8504 - val_loss: 0.6492 - val_accuracy: 0.6957\n",
      "Epoch 439/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2999 - accuracy: 0.8898 - val_loss: 0.6532 - val_accuracy: 0.6957\n",
      "Epoch 440/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3474 - accuracy: 0.8346 - val_loss: 0.6541 - val_accuracy: 0.6957\n",
      "Epoch 441/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.2843 - accuracy: 0.8583 - val_loss: 0.6523 - val_accuracy: 0.6957\n",
      "Epoch 442/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3306 - accuracy: 0.8425 - val_loss: 0.6476 - val_accuracy: 0.6957\n",
      "Epoch 443/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3058 - accuracy: 0.8583 - val_loss: 0.6466 - val_accuracy: 0.6957\n",
      "Epoch 444/500\n",
      "127/127 [==============================] - 0s 79us/step - loss: 0.3363 - accuracy: 0.8898 - val_loss: 0.6508 - val_accuracy: 0.6957\n",
      "Epoch 445/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3531 - accuracy: 0.8189 - val_loss: 0.6477 - val_accuracy: 0.6957\n",
      "Epoch 446/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 0.3538 - accuracy: 0.8504 - val_loss: 0.6436 - val_accuracy: 0.6957\n",
      "Epoch 447/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3102 - accuracy: 0.8661 - val_loss: 0.6469 - val_accuracy: 0.6957\n",
      "Epoch 448/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3354 - accuracy: 0.8583 - val_loss: 0.6490 - val_accuracy: 0.6957\n",
      "Epoch 449/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3277 - accuracy: 0.8583 - val_loss: 0.6410 - val_accuracy: 0.6957\n",
      "Epoch 450/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3346 - accuracy: 0.8425 - val_loss: 0.6419 - val_accuracy: 0.6957\n",
      "Epoch 451/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3122 - accuracy: 0.8819 - val_loss: 0.6438 - val_accuracy: 0.6957\n",
      "Epoch 452/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3737 - accuracy: 0.8110 - val_loss: 0.6409 - val_accuracy: 0.6957\n",
      "Epoch 453/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3262 - accuracy: 0.8583 - val_loss: 0.6419 - val_accuracy: 0.6957\n",
      "Epoch 454/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3679 - accuracy: 0.8110 - val_loss: 0.6392 - val_accuracy: 0.6957\n",
      "Epoch 455/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3277 - accuracy: 0.8819 - val_loss: 0.6352 - val_accuracy: 0.6957\n",
      "Epoch 456/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2877 - accuracy: 0.8976 - val_loss: 0.6371 - val_accuracy: 0.6957\n",
      "Epoch 457/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3269 - accuracy: 0.8819 - val_loss: 0.6382 - val_accuracy: 0.6957\n",
      "Epoch 458/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.2723 - accuracy: 0.9213 - val_loss: 0.6399 - val_accuracy: 0.6957\n",
      "Epoch 459/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3075 - accuracy: 0.8976 - val_loss: 0.6445 - val_accuracy: 0.6957\n",
      "Epoch 460/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3053 - accuracy: 0.9134 - val_loss: 0.6420 - val_accuracy: 0.6957\n",
      "Epoch 461/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3443 - accuracy: 0.8740 - val_loss: 0.6435 - val_accuracy: 0.6957\n",
      "Epoch 462/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3302 - accuracy: 0.8583 - val_loss: 0.6410 - val_accuracy: 0.6957\n",
      "Epoch 463/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3389 - accuracy: 0.8189 - val_loss: 0.6340 - val_accuracy: 0.6957\n",
      "Epoch 464/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3482 - accuracy: 0.7953 - val_loss: 0.6346 - val_accuracy: 0.6957\n",
      "Epoch 465/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3474 - accuracy: 0.8425 - val_loss: 0.6363 - val_accuracy: 0.6957\n",
      "Epoch 466/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3183 - accuracy: 0.8504 - val_loss: 0.6343 - val_accuracy: 0.6957\n",
      "Epoch 467/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2829 - accuracy: 0.9134 - val_loss: 0.6343 - val_accuracy: 0.6957\n",
      "Epoch 468/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3297 - accuracy: 0.8504 - val_loss: 0.6335 - val_accuracy: 0.6957\n",
      "Epoch 469/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3151 - accuracy: 0.8346 - val_loss: 0.6353 - val_accuracy: 0.6957\n",
      "Epoch 470/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3434 - accuracy: 0.8504 - val_loss: 0.6330 - val_accuracy: 0.6957\n",
      "Epoch 471/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2851 - accuracy: 0.9055 - val_loss: 0.6295 - val_accuracy: 0.6957\n",
      "Epoch 472/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3212 - accuracy: 0.8583 - val_loss: 0.6326 - val_accuracy: 0.6957\n",
      "Epoch 473/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3075 - accuracy: 0.8740 - val_loss: 0.6299 - val_accuracy: 0.6957\n",
      "Epoch 474/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3440 - accuracy: 0.8268 - val_loss: 0.6301 - val_accuracy: 0.6957\n",
      "Epoch 475/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3282 - accuracy: 0.8898 - val_loss: 0.6366 - val_accuracy: 0.6957\n",
      "Epoch 476/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2900 - accuracy: 0.8819 - val_loss: 0.6296 - val_accuracy: 0.6957\n",
      "Epoch 477/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3286 - accuracy: 0.8110 - val_loss: 0.6245 - val_accuracy: 0.6957\n",
      "Epoch 478/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3229 - accuracy: 0.8425 - val_loss: 0.6271 - val_accuracy: 0.6957\n",
      "Epoch 479/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3195 - accuracy: 0.8583 - val_loss: 0.6262 - val_accuracy: 0.6957\n",
      "Epoch 480/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3415 - accuracy: 0.8425 - val_loss: 0.6292 - val_accuracy: 0.6957\n",
      "Epoch 481/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3159 - accuracy: 0.8504 - val_loss: 0.6280 - val_accuracy: 0.6957\n",
      "Epoch 482/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.3028 - accuracy: 0.8740 - val_loss: 0.6235 - val_accuracy: 0.6957\n",
      "Epoch 483/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2979 - accuracy: 0.8898 - val_loss: 0.6218 - val_accuracy: 0.6957\n",
      "Epoch 484/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3306 - accuracy: 0.8268 - val_loss: 0.6158 - val_accuracy: 0.6957\n",
      "Epoch 485/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 0.2948 - accuracy: 0.8740 - val_loss: 0.6149 - val_accuracy: 0.6957\n",
      "Epoch 486/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3226 - accuracy: 0.8583 - val_loss: 0.6147 - val_accuracy: 0.6957\n",
      "Epoch 487/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.3043 - accuracy: 0.8661 - val_loss: 0.6069 - val_accuracy: 0.7391\n",
      "Epoch 488/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3042 - accuracy: 0.8898 - val_loss: 0.6077 - val_accuracy: 0.7391\n",
      "Epoch 489/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3162 - accuracy: 0.8661 - val_loss: 0.6123 - val_accuracy: 0.6957\n",
      "Epoch 490/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 0.2988 - accuracy: 0.8898 - val_loss: 0.6091 - val_accuracy: 0.6957\n",
      "Epoch 491/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3242 - accuracy: 0.8425 - val_loss: 0.6049 - val_accuracy: 0.7826\n",
      "Epoch 492/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2914 - accuracy: 0.8661 - val_loss: 0.6028 - val_accuracy: 0.7826\n",
      "Epoch 493/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3373 - accuracy: 0.8504 - val_loss: 0.6031 - val_accuracy: 0.7826\n",
      "Epoch 494/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3121 - accuracy: 0.8504 - val_loss: 0.6016 - val_accuracy: 0.7826\n",
      "Epoch 495/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2918 - accuracy: 0.8740 - val_loss: 0.5978 - val_accuracy: 0.7826\n",
      "Epoch 496/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3131 - accuracy: 0.8740 - val_loss: 0.5993 - val_accuracy: 0.7826\n",
      "Epoch 497/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 0.3048 - accuracy: 0.8661 - val_loss: 0.5978 - val_accuracy: 0.7826\n",
      "Epoch 498/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2798 - accuracy: 0.9055 - val_loss: 0.5950 - val_accuracy: 0.7826\n",
      "Epoch 499/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.3048 - accuracy: 0.8740 - val_loss: 0.6028 - val_accuracy: 0.7391\n",
      "Epoch 500/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 0.2898 - accuracy: 0.8740 - val_loss: 0.6013 - val_accuracy: 0.7826\n"
     ]
    }
   ],
   "source": [
    "history01=model_drop.fit(input_data,correct_data,validation_split=0.15,epochs=500)\n",
    "ans=model_drop.predict(input_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy : 93.33%\n"
     ]
    }
   ],
   "source": [
    "score2=model_drop.evaluate(input_test,correct_test,verbose=0)\n",
    "print(\"Test accuracy : %.2f%%\" %(score2[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 127 samples, validate on 23 samples\n",
      "Epoch 1/500\n",
      "127/127 [==============================] - 0s 683us/step - loss: 1.2177 - accuracy: 0.3937 - val_loss: 1.3752 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.2215 - accuracy: 0.3937 - val_loss: 1.3646 - val_accuracy: 0.0000e+00\n",
      "Epoch 3/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.1783 - accuracy: 0.3937 - val_loss: 1.3612 - val_accuracy: 0.0000e+00\n",
      "Epoch 4/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.1580 - accuracy: 0.3937 - val_loss: 1.3588 - val_accuracy: 0.0000e+00\n",
      "Epoch 5/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.1567 - accuracy: 0.3937 - val_loss: 1.3598 - val_accuracy: 0.0000e+00\n",
      "Epoch 6/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.1372 - accuracy: 0.3937 - val_loss: 1.3618 - val_accuracy: 0.0000e+00\n",
      "Epoch 7/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.1228 - accuracy: 0.3937 - val_loss: 1.3643 - val_accuracy: 0.0000e+00\n",
      "Epoch 8/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.1106 - accuracy: 0.3937 - val_loss: 1.3685 - val_accuracy: 0.0000e+00\n",
      "Epoch 9/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.1068 - accuracy: 0.3937 - val_loss: 1.3738 - val_accuracy: 0.0000e+00\n",
      "Epoch 10/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.1042 - accuracy: 0.3937 - val_loss: 1.3800 - val_accuracy: 0.0000e+00\n",
      "Epoch 11/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0941 - accuracy: 0.3858 - val_loss: 1.3881 - val_accuracy: 0.0000e+00\n",
      "Epoch 12/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0953 - accuracy: 0.3937 - val_loss: 1.3971 - val_accuracy: 0.0000e+00\n",
      "Epoch 13/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0980 - accuracy: 0.3858 - val_loss: 1.4055 - val_accuracy: 0.0000e+00\n",
      "Epoch 14/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0868 - accuracy: 0.4016 - val_loss: 1.4095 - val_accuracy: 0.0000e+00\n",
      "Epoch 15/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0773 - accuracy: 0.4252 - val_loss: 1.4207 - val_accuracy: 0.0000e+00\n",
      "Epoch 16/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0570 - accuracy: 0.4016 - val_loss: 1.4271 - val_accuracy: 0.0000e+00\n",
      "Epoch 17/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0667 - accuracy: 0.3858 - val_loss: 1.4325 - val_accuracy: 0.0000e+00\n",
      "Epoch 18/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0792 - accuracy: 0.4094 - val_loss: 1.4405 - val_accuracy: 0.0000e+00\n",
      "Epoch 19/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0812 - accuracy: 0.3780 - val_loss: 1.4463 - val_accuracy: 0.0000e+00\n",
      "Epoch 20/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0730 - accuracy: 0.3937 - val_loss: 1.4513 - val_accuracy: 0.0000e+00\n",
      "Epoch 21/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0751 - accuracy: 0.4173 - val_loss: 1.4548 - val_accuracy: 0.0000e+00\n",
      "Epoch 22/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0795 - accuracy: 0.4331 - val_loss: 1.4605 - val_accuracy: 0.0000e+00\n",
      "Epoch 23/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0591 - accuracy: 0.4094 - val_loss: 1.4656 - val_accuracy: 0.0000e+00\n",
      "Epoch 24/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0805 - accuracy: 0.3780 - val_loss: 1.4697 - val_accuracy: 0.0000e+00\n",
      "Epoch 25/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0586 - accuracy: 0.3858 - val_loss: 1.4727 - val_accuracy: 0.0000e+00\n",
      "Epoch 26/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0746 - accuracy: 0.3858 - val_loss: 1.4782 - val_accuracy: 0.0000e+00\n",
      "Epoch 27/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0800 - accuracy: 0.3858 - val_loss: 1.4832 - val_accuracy: 0.0000e+00\n",
      "Epoch 28/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 1.0805 - accuracy: 0.4252 - val_loss: 1.4882 - val_accuracy: 0.0000e+00\n",
      "Epoch 29/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0777 - accuracy: 0.4331 - val_loss: 1.4916 - val_accuracy: 0.0000e+00\n",
      "Epoch 30/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0597 - accuracy: 0.4331 - val_loss: 1.4948 - val_accuracy: 0.0000e+00\n",
      "Epoch 31/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0654 - accuracy: 0.3465 - val_loss: 1.4969 - val_accuracy: 0.0000e+00\n",
      "Epoch 32/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0593 - accuracy: 0.3858 - val_loss: 1.5007 - val_accuracy: 0.0000e+00\n",
      "Epoch 33/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0722 - accuracy: 0.4331 - val_loss: 1.5043 - val_accuracy: 0.0000e+00\n",
      "Epoch 34/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 1.0605 - accuracy: 0.3858 - val_loss: 1.5065 - val_accuracy: 0.0000e+00\n",
      "Epoch 35/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0804 - accuracy: 0.4094 - val_loss: 1.5083 - val_accuracy: 0.0000e+00\n",
      "Epoch 36/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0694 - accuracy: 0.3622 - val_loss: 1.5100 - val_accuracy: 0.0000e+00\n",
      "Epoch 37/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0881 - accuracy: 0.2992 - val_loss: 1.5127 - val_accuracy: 0.0000e+00\n",
      "Epoch 38/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0779 - accuracy: 0.3228 - val_loss: 1.5170 - val_accuracy: 0.0000e+00\n",
      "Epoch 39/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0632 - accuracy: 0.4252 - val_loss: 1.5179 - val_accuracy: 0.0000e+00\n",
      "Epoch 40/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0601 - accuracy: 0.4173 - val_loss: 1.5211 - val_accuracy: 0.0000e+00\n",
      "Epoch 41/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 1.0721 - accuracy: 0.4016 - val_loss: 1.5229 - val_accuracy: 0.0000e+00\n",
      "Epoch 42/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0727 - accuracy: 0.3858 - val_loss: 1.5238 - val_accuracy: 0.0000e+00\n",
      "Epoch 43/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0705 - accuracy: 0.3465 - val_loss: 1.5252 - val_accuracy: 0.0000e+00\n",
      "Epoch 44/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 1.0768 - accuracy: 0.3307 - val_loss: 1.5250 - val_accuracy: 0.0000e+00\n",
      "Epoch 45/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0875 - accuracy: 0.2598 - val_loss: 1.5247 - val_accuracy: 0.0000e+00\n",
      "Epoch 46/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0603 - accuracy: 0.3937 - val_loss: 1.5282 - val_accuracy: 0.0000e+00\n",
      "Epoch 47/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0562 - accuracy: 0.3780 - val_loss: 1.5267 - val_accuracy: 0.0000e+00\n",
      "Epoch 48/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0330 - accuracy: 0.5197 - val_loss: 1.5294 - val_accuracy: 0.0000e+00\n",
      "Epoch 49/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0446 - accuracy: 0.4882 - val_loss: 1.5297 - val_accuracy: 0.0000e+00\n",
      "Epoch 50/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0687 - accuracy: 0.4252 - val_loss: 1.5308 - val_accuracy: 0.0000e+00\n",
      "Epoch 51/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0719 - accuracy: 0.3780 - val_loss: 1.5308 - val_accuracy: 0.0000e+00\n",
      "Epoch 52/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0794 - accuracy: 0.3543 - val_loss: 1.5316 - val_accuracy: 0.0000e+00\n",
      "Epoch 53/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0671 - accuracy: 0.3780 - val_loss: 1.5324 - val_accuracy: 0.0000e+00\n",
      "Epoch 54/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0805 - accuracy: 0.2835 - val_loss: 1.5327 - val_accuracy: 0.0000e+00\n",
      "Epoch 55/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 1.0596 - accuracy: 0.4094 - val_loss: 1.5345 - val_accuracy: 0.0000e+00\n",
      "Epoch 56/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0691 - accuracy: 0.4016 - val_loss: 1.5359 - val_accuracy: 0.0000e+00\n",
      "Epoch 57/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0675 - accuracy: 0.3937 - val_loss: 1.5373 - val_accuracy: 0.0000e+00\n",
      "Epoch 58/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0743 - accuracy: 0.3701 - val_loss: 1.5379 - val_accuracy: 0.0000e+00\n",
      "Epoch 59/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0814 - accuracy: 0.3701 - val_loss: 1.5373 - val_accuracy: 0.0000e+00\n",
      "Epoch 60/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0601 - accuracy: 0.3071 - val_loss: 1.5383 - val_accuracy: 0.0000e+00\n",
      "Epoch 61/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 1.0878 - accuracy: 0.3150 - val_loss: 1.5382 - val_accuracy: 0.0000e+00\n",
      "Epoch 62/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0613 - accuracy: 0.3937 - val_loss: 1.5378 - val_accuracy: 0.0000e+00\n",
      "Epoch 63/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0806 - accuracy: 0.3307 - val_loss: 1.5357 - val_accuracy: 0.0000e+00\n",
      "Epoch 64/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0656 - accuracy: 0.3543 - val_loss: 1.5355 - val_accuracy: 0.0000e+00\n",
      "Epoch 65/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0746 - accuracy: 0.3386 - val_loss: 1.5341 - val_accuracy: 0.0000e+00\n",
      "Epoch 66/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0698 - accuracy: 0.3780 - val_loss: 1.5341 - val_accuracy: 0.0000e+00\n",
      "Epoch 67/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0628 - accuracy: 0.3701 - val_loss: 1.5341 - val_accuracy: 0.0000e+00\n",
      "Epoch 68/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0849 - accuracy: 0.3780 - val_loss: 1.5336 - val_accuracy: 0.0000e+00\n",
      "Epoch 69/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0555 - accuracy: 0.4409 - val_loss: 1.5341 - val_accuracy: 0.0000e+00\n",
      "Epoch 70/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 1.0621 - accuracy: 0.4094 - val_loss: 1.5347 - val_accuracy: 0.0000e+00\n",
      "Epoch 71/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0711 - accuracy: 0.3701 - val_loss: 1.5352 - val_accuracy: 0.0000e+00\n",
      "Epoch 72/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0607 - accuracy: 0.4724 - val_loss: 1.5349 - val_accuracy: 0.0000e+00\n",
      "Epoch 73/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0577 - accuracy: 0.4331 - val_loss: 1.5354 - val_accuracy: 0.0000e+00\n",
      "Epoch 74/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0606 - accuracy: 0.4252 - val_loss: 1.5366 - val_accuracy: 0.0000e+00\n",
      "Epoch 75/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0566 - accuracy: 0.4567 - val_loss: 1.5365 - val_accuracy: 0.0000e+00\n",
      "Epoch 76/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0626 - accuracy: 0.3543 - val_loss: 1.5364 - val_accuracy: 0.0000e+00\n",
      "Epoch 77/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0671 - accuracy: 0.3701 - val_loss: 1.5349 - val_accuracy: 0.0000e+00\n",
      "Epoch 78/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0710 - accuracy: 0.3858 - val_loss: 1.5365 - val_accuracy: 0.0000e+00\n",
      "Epoch 79/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0420 - accuracy: 0.5039 - val_loss: 1.5374 - val_accuracy: 0.0000e+00\n",
      "Epoch 80/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0651 - accuracy: 0.4331 - val_loss: 1.5373 - val_accuracy: 0.0000e+00\n",
      "Epoch 81/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0649 - accuracy: 0.4094 - val_loss: 1.5378 - val_accuracy: 0.0000e+00\n",
      "Epoch 82/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0533 - accuracy: 0.4646 - val_loss: 1.5388 - val_accuracy: 0.0000e+00\n",
      "Epoch 83/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0685 - accuracy: 0.4252 - val_loss: 1.5390 - val_accuracy: 0.0000e+00\n",
      "Epoch 84/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 1.0563 - accuracy: 0.4646 - val_loss: 1.5393 - val_accuracy: 0.0000e+00\n",
      "Epoch 85/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0807 - accuracy: 0.3701 - val_loss: 1.5376 - val_accuracy: 0.0000e+00\n",
      "Epoch 86/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0771 - accuracy: 0.3701 - val_loss: 1.5390 - val_accuracy: 0.0000e+00\n",
      "Epoch 87/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0539 - accuracy: 0.4331 - val_loss: 1.5391 - val_accuracy: 0.0000e+00\n",
      "Epoch 88/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0617 - accuracy: 0.4016 - val_loss: 1.5384 - val_accuracy: 0.0000e+00\n",
      "Epoch 89/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0651 - accuracy: 0.3622 - val_loss: 1.5385 - val_accuracy: 0.0000e+00\n",
      "Epoch 90/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0709 - accuracy: 0.4016 - val_loss: 1.5378 - val_accuracy: 0.0000e+00\n",
      "Epoch 91/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 1.0647 - accuracy: 0.3780 - val_loss: 1.5376 - val_accuracy: 0.0000e+00\n",
      "Epoch 92/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0718 - accuracy: 0.3858 - val_loss: 1.5390 - val_accuracy: 0.0000e+00\n",
      "Epoch 93/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0484 - accuracy: 0.4409 - val_loss: 1.5389 - val_accuracy: 0.0000e+00\n",
      "Epoch 94/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0784 - accuracy: 0.3780 - val_loss: 1.5394 - val_accuracy: 0.0000e+00\n",
      "Epoch 95/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0814 - accuracy: 0.3701 - val_loss: 1.5389 - val_accuracy: 0.0000e+00\n",
      "Epoch 96/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0748 - accuracy: 0.3622 - val_loss: 1.5393 - val_accuracy: 0.0000e+00\n",
      "Epoch 97/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0734 - accuracy: 0.3465 - val_loss: 1.5406 - val_accuracy: 0.0000e+00\n",
      "Epoch 98/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0582 - accuracy: 0.4016 - val_loss: 1.5408 - val_accuracy: 0.0000e+00\n",
      "Epoch 99/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0757 - accuracy: 0.3622 - val_loss: 1.5415 - val_accuracy: 0.0000e+00\n",
      "Epoch 100/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0678 - accuracy: 0.4646 - val_loss: 1.5415 - val_accuracy: 0.0000e+00\n",
      "Epoch 101/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0632 - accuracy: 0.4016 - val_loss: 1.5431 - val_accuracy: 0.0000e+00\n",
      "Epoch 102/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0649 - accuracy: 0.4173 - val_loss: 1.5435 - val_accuracy: 0.0000e+00\n",
      "Epoch 103/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 1.0867 - accuracy: 0.3228 - val_loss: 1.5432 - val_accuracy: 0.0000e+00\n",
      "Epoch 104/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0630 - accuracy: 0.3780 - val_loss: 1.5422 - val_accuracy: 0.0000e+00\n",
      "Epoch 105/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0579 - accuracy: 0.4173 - val_loss: 1.5403 - val_accuracy: 0.0000e+00\n",
      "Epoch 106/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0550 - accuracy: 0.4252 - val_loss: 1.5412 - val_accuracy: 0.0000e+00\n",
      "Epoch 107/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0748 - accuracy: 0.3858 - val_loss: 1.5410 - val_accuracy: 0.0000e+00\n",
      "Epoch 108/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 1.0615 - accuracy: 0.4016 - val_loss: 1.5409 - val_accuracy: 0.0000e+00\n",
      "Epoch 109/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0723 - accuracy: 0.3386 - val_loss: 1.5426 - val_accuracy: 0.0000e+00\n",
      "Epoch 110/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0705 - accuracy: 0.3937 - val_loss: 1.5428 - val_accuracy: 0.0000e+00\n",
      "Epoch 111/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0519 - accuracy: 0.3780 - val_loss: 1.5430 - val_accuracy: 0.0000e+00\n",
      "Epoch 112/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0685 - accuracy: 0.4016 - val_loss: 1.5418 - val_accuracy: 0.0000e+00\n",
      "Epoch 113/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0619 - accuracy: 0.3937 - val_loss: 1.5408 - val_accuracy: 0.0000e+00\n",
      "Epoch 114/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0552 - accuracy: 0.4094 - val_loss: 1.5425 - val_accuracy: 0.0000e+00\n",
      "Epoch 115/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0621 - accuracy: 0.3701 - val_loss: 1.5421 - val_accuracy: 0.0000e+00\n",
      "Epoch 116/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0677 - accuracy: 0.4173 - val_loss: 1.5414 - val_accuracy: 0.0000e+00\n",
      "Epoch 117/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0776 - accuracy: 0.3465 - val_loss: 1.5424 - val_accuracy: 0.0000e+00\n",
      "Epoch 118/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0563 - accuracy: 0.4173 - val_loss: 1.5434 - val_accuracy: 0.0000e+00\n",
      "Epoch 119/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0483 - accuracy: 0.4882 - val_loss: 1.5448 - val_accuracy: 0.0000e+00\n",
      "Epoch 120/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0710 - accuracy: 0.4094 - val_loss: 1.5439 - val_accuracy: 0.0000e+00\n",
      "Epoch 121/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0705 - accuracy: 0.4016 - val_loss: 1.5433 - val_accuracy: 0.0000e+00\n",
      "Epoch 122/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0585 - accuracy: 0.4331 - val_loss: 1.5435 - val_accuracy: 0.0000e+00\n",
      "Epoch 123/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0589 - accuracy: 0.4252 - val_loss: 1.5424 - val_accuracy: 0.0000e+00\n",
      "Epoch 124/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0629 - accuracy: 0.3622 - val_loss: 1.5401 - val_accuracy: 0.0000e+00\n",
      "Epoch 125/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0569 - accuracy: 0.3622 - val_loss: 1.5397 - val_accuracy: 0.0000e+00\n",
      "Epoch 126/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0536 - accuracy: 0.4094 - val_loss: 1.5409 - val_accuracy: 0.0000e+00\n",
      "Epoch 127/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0682 - accuracy: 0.3937 - val_loss: 1.5411 - val_accuracy: 0.0000e+00\n",
      "Epoch 128/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0791 - accuracy: 0.3307 - val_loss: 1.5403 - val_accuracy: 0.0000e+00\n",
      "Epoch 129/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0536 - accuracy: 0.4252 - val_loss: 1.5396 - val_accuracy: 0.0000e+00\n",
      "Epoch 130/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0644 - accuracy: 0.4173 - val_loss: 1.5391 - val_accuracy: 0.0000e+00\n",
      "Epoch 131/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0428 - accuracy: 0.4488 - val_loss: 1.5376 - val_accuracy: 0.0000e+00\n",
      "Epoch 132/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0524 - accuracy: 0.4882 - val_loss: 1.5383 - val_accuracy: 0.0000e+00\n",
      "Epoch 133/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0441 - accuracy: 0.4724 - val_loss: 1.5384 - val_accuracy: 0.0000e+00\n",
      "Epoch 134/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0546 - accuracy: 0.4488 - val_loss: 1.5380 - val_accuracy: 0.0000e+00\n",
      "Epoch 135/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0499 - accuracy: 0.4803 - val_loss: 1.5392 - val_accuracy: 0.0000e+00\n",
      "Epoch 136/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0577 - accuracy: 0.4331 - val_loss: 1.5400 - val_accuracy: 0.0000e+00\n",
      "Epoch 137/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0758 - accuracy: 0.4409 - val_loss: 1.5398 - val_accuracy: 0.0000e+00\n",
      "Epoch 138/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0694 - accuracy: 0.4173 - val_loss: 1.5398 - val_accuracy: 0.0000e+00\n",
      "Epoch 139/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0585 - accuracy: 0.4016 - val_loss: 1.5412 - val_accuracy: 0.0000e+00\n",
      "Epoch 140/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0604 - accuracy: 0.3937 - val_loss: 1.5402 - val_accuracy: 0.0000e+00\n",
      "Epoch 141/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0564 - accuracy: 0.4488 - val_loss: 1.5409 - val_accuracy: 0.0000e+00\n",
      "Epoch 142/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0476 - accuracy: 0.4488 - val_loss: 1.5395 - val_accuracy: 0.0000e+00\n",
      "Epoch 143/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0578 - accuracy: 0.3780 - val_loss: 1.5396 - val_accuracy: 0.0000e+00\n",
      "Epoch 144/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0399 - accuracy: 0.4882 - val_loss: 1.5377 - val_accuracy: 0.0000e+00\n",
      "Epoch 145/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0713 - accuracy: 0.3858 - val_loss: 1.5379 - val_accuracy: 0.0000e+00\n",
      "Epoch 146/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0597 - accuracy: 0.4724 - val_loss: 1.5392 - val_accuracy: 0.0000e+00\n",
      "Epoch 147/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0574 - accuracy: 0.4016 - val_loss: 1.5369 - val_accuracy: 0.0000e+00\n",
      "Epoch 148/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0694 - accuracy: 0.3780 - val_loss: 1.5382 - val_accuracy: 0.0000e+00\n",
      "Epoch 149/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0621 - accuracy: 0.4882 - val_loss: 1.5374 - val_accuracy: 0.0000e+00\n",
      "Epoch 150/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0721 - accuracy: 0.4094 - val_loss: 1.5377 - val_accuracy: 0.0000e+00\n",
      "Epoch 151/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0655 - accuracy: 0.3701 - val_loss: 1.5369 - val_accuracy: 0.0000e+00\n",
      "Epoch 152/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0616 - accuracy: 0.4173 - val_loss: 1.5372 - val_accuracy: 0.0000e+00\n",
      "Epoch 153/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0626 - accuracy: 0.4094 - val_loss: 1.5382 - val_accuracy: 0.0000e+00\n",
      "Epoch 154/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0583 - accuracy: 0.4094 - val_loss: 1.5374 - val_accuracy: 0.0000e+00\n",
      "Epoch 155/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0840 - accuracy: 0.3780 - val_loss: 1.5360 - val_accuracy: 0.0000e+00\n",
      "Epoch 156/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0559 - accuracy: 0.4803 - val_loss: 1.5367 - val_accuracy: 0.0000e+00\n",
      "Epoch 157/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0603 - accuracy: 0.4173 - val_loss: 1.5362 - val_accuracy: 0.0000e+00\n",
      "Epoch 158/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0463 - accuracy: 0.4646 - val_loss: 1.5360 - val_accuracy: 0.0000e+00\n",
      "Epoch 159/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0714 - accuracy: 0.3465 - val_loss: 1.5368 - val_accuracy: 0.0000e+00\n",
      "Epoch 160/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0480 - accuracy: 0.4488 - val_loss: 1.5353 - val_accuracy: 0.0000e+00\n",
      "Epoch 161/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 1.0518 - accuracy: 0.4173 - val_loss: 1.5351 - val_accuracy: 0.0000e+00\n",
      "Epoch 162/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0550 - accuracy: 0.4016 - val_loss: 1.5317 - val_accuracy: 0.0000e+00\n",
      "Epoch 163/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0433 - accuracy: 0.4252 - val_loss: 1.5320 - val_accuracy: 0.0000e+00\n",
      "Epoch 164/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0534 - accuracy: 0.4252 - val_loss: 1.5317 - val_accuracy: 0.0000e+00\n",
      "Epoch 165/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0824 - accuracy: 0.3228 - val_loss: 1.5315 - val_accuracy: 0.0000e+00\n",
      "Epoch 166/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0563 - accuracy: 0.4252 - val_loss: 1.5318 - val_accuracy: 0.0000e+00\n",
      "Epoch 167/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 1.0664 - accuracy: 0.4016 - val_loss: 1.5323 - val_accuracy: 0.0000e+00\n",
      "Epoch 168/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0546 - accuracy: 0.4252 - val_loss: 1.5314 - val_accuracy: 0.0000e+00\n",
      "Epoch 169/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0746 - accuracy: 0.3701 - val_loss: 1.5316 - val_accuracy: 0.0000e+00\n",
      "Epoch 170/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 1.0662 - accuracy: 0.4016 - val_loss: 1.5342 - val_accuracy: 0.0000e+00\n",
      "Epoch 171/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0568 - accuracy: 0.4252 - val_loss: 1.5343 - val_accuracy: 0.0000e+00\n",
      "Epoch 172/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0570 - accuracy: 0.3937 - val_loss: 1.5333 - val_accuracy: 0.0000e+00\n",
      "Epoch 173/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0627 - accuracy: 0.4094 - val_loss: 1.5315 - val_accuracy: 0.0000e+00\n",
      "Epoch 174/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0633 - accuracy: 0.4488 - val_loss: 1.5311 - val_accuracy: 0.0000e+00\n",
      "Epoch 175/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0489 - accuracy: 0.4331 - val_loss: 1.5328 - val_accuracy: 0.0000e+00\n",
      "Epoch 176/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0545 - accuracy: 0.4173 - val_loss: 1.5328 - val_accuracy: 0.0000e+00\n",
      "Epoch 177/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0532 - accuracy: 0.4488 - val_loss: 1.5338 - val_accuracy: 0.0000e+00\n",
      "Epoch 178/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0542 - accuracy: 0.4016 - val_loss: 1.5343 - val_accuracy: 0.0000e+00\n",
      "Epoch 179/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0663 - accuracy: 0.3701 - val_loss: 1.5358 - val_accuracy: 0.0000e+00\n",
      "Epoch 180/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0635 - accuracy: 0.4016 - val_loss: 1.5350 - val_accuracy: 0.0000e+00\n",
      "Epoch 181/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0544 - accuracy: 0.4409 - val_loss: 1.5323 - val_accuracy: 0.0000e+00\n",
      "Epoch 182/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0702 - accuracy: 0.3780 - val_loss: 1.5313 - val_accuracy: 0.0000e+00\n",
      "Epoch 183/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0666 - accuracy: 0.4331 - val_loss: 1.5309 - val_accuracy: 0.0000e+00\n",
      "Epoch 184/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0661 - accuracy: 0.4016 - val_loss: 1.5314 - val_accuracy: 0.0000e+00\n",
      "Epoch 185/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0691 - accuracy: 0.4016 - val_loss: 1.5318 - val_accuracy: 0.0000e+00\n",
      "Epoch 186/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0709 - accuracy: 0.4173 - val_loss: 1.5309 - val_accuracy: 0.0000e+00\n",
      "Epoch 187/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0550 - accuracy: 0.4409 - val_loss: 1.5314 - val_accuracy: 0.0000e+00\n",
      "Epoch 188/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 1.0715 - accuracy: 0.3780 - val_loss: 1.5307 - val_accuracy: 0.0000e+00\n",
      "Epoch 189/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0531 - accuracy: 0.3858 - val_loss: 1.5295 - val_accuracy: 0.0000e+00\n",
      "Epoch 190/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0418 - accuracy: 0.4173 - val_loss: 1.5304 - val_accuracy: 0.0000e+00\n",
      "Epoch 191/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0674 - accuracy: 0.3386 - val_loss: 1.5308 - val_accuracy: 0.0000e+00\n",
      "Epoch 192/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0543 - accuracy: 0.3937 - val_loss: 1.5312 - val_accuracy: 0.0000e+00\n",
      "Epoch 193/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0625 - accuracy: 0.4173 - val_loss: 1.5319 - val_accuracy: 0.0000e+00\n",
      "Epoch 194/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0582 - accuracy: 0.4094 - val_loss: 1.5317 - val_accuracy: 0.0000e+00\n",
      "Epoch 195/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0478 - accuracy: 0.4646 - val_loss: 1.5335 - val_accuracy: 0.0000e+00\n",
      "Epoch 196/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0637 - accuracy: 0.4173 - val_loss: 1.5346 - val_accuracy: 0.0000e+00\n",
      "Epoch 197/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0650 - accuracy: 0.4331 - val_loss: 1.5329 - val_accuracy: 0.0000e+00\n",
      "Epoch 198/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0484 - accuracy: 0.4646 - val_loss: 1.5327 - val_accuracy: 0.0000e+00\n",
      "Epoch 199/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0585 - accuracy: 0.4016 - val_loss: 1.5331 - val_accuracy: 0.0000e+00\n",
      "Epoch 200/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0503 - accuracy: 0.4409 - val_loss: 1.5332 - val_accuracy: 0.0000e+00\n",
      "Epoch 201/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0552 - accuracy: 0.4488 - val_loss: 1.5334 - val_accuracy: 0.0000e+00\n",
      "Epoch 202/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0678 - accuracy: 0.3780 - val_loss: 1.5337 - val_accuracy: 0.0000e+00\n",
      "Epoch 203/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0625 - accuracy: 0.3701 - val_loss: 1.5348 - val_accuracy: 0.0000e+00\n",
      "Epoch 204/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0550 - accuracy: 0.4567 - val_loss: 1.5353 - val_accuracy: 0.0000e+00\n",
      "Epoch 205/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0666 - accuracy: 0.4016 - val_loss: 1.5341 - val_accuracy: 0.0000e+00\n",
      "Epoch 206/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0639 - accuracy: 0.3701 - val_loss: 1.5341 - val_accuracy: 0.0000e+00\n",
      "Epoch 207/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0608 - accuracy: 0.3780 - val_loss: 1.5349 - val_accuracy: 0.0000e+00\n",
      "Epoch 208/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0751 - accuracy: 0.4016 - val_loss: 1.5346 - val_accuracy: 0.0000e+00\n",
      "Epoch 209/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0540 - accuracy: 0.4567 - val_loss: 1.5331 - val_accuracy: 0.0000e+00\n",
      "Epoch 210/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 1.0615 - accuracy: 0.4567 - val_loss: 1.5329 - val_accuracy: 0.0000e+00\n",
      "Epoch 211/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0596 - accuracy: 0.4252 - val_loss: 1.5339 - val_accuracy: 0.0000e+00\n",
      "Epoch 212/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0558 - accuracy: 0.3780 - val_loss: 1.5324 - val_accuracy: 0.0000e+00\n",
      "Epoch 213/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0520 - accuracy: 0.4173 - val_loss: 1.5347 - val_accuracy: 0.0000e+00\n",
      "Epoch 214/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0620 - accuracy: 0.4488 - val_loss: 1.5325 - val_accuracy: 0.0000e+00\n",
      "Epoch 215/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0566 - accuracy: 0.4331 - val_loss: 1.5335 - val_accuracy: 0.0000e+00\n",
      "Epoch 216/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0445 - accuracy: 0.4252 - val_loss: 1.5323 - val_accuracy: 0.0000e+00\n",
      "Epoch 217/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0658 - accuracy: 0.3622 - val_loss: 1.5316 - val_accuracy: 0.0000e+00\n",
      "Epoch 218/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0647 - accuracy: 0.3858 - val_loss: 1.5322 - val_accuracy: 0.0000e+00\n",
      "Epoch 219/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0583 - accuracy: 0.3858 - val_loss: 1.5325 - val_accuracy: 0.0000e+00\n",
      "Epoch 220/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0372 - accuracy: 0.4882 - val_loss: 1.5314 - val_accuracy: 0.0000e+00\n",
      "Epoch 221/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0607 - accuracy: 0.3780 - val_loss: 1.5318 - val_accuracy: 0.0000e+00\n",
      "Epoch 222/500\n",
      "127/127 [==============================] - 0s 24us/step - loss: 1.0742 - accuracy: 0.3150 - val_loss: 1.5328 - val_accuracy: 0.0000e+00\n",
      "Epoch 223/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0456 - accuracy: 0.4173 - val_loss: 1.5328 - val_accuracy: 0.0000e+00\n",
      "Epoch 224/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0533 - accuracy: 0.4567 - val_loss: 1.5321 - val_accuracy: 0.0000e+00\n",
      "Epoch 225/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0651 - accuracy: 0.3701 - val_loss: 1.5327 - val_accuracy: 0.0000e+00\n",
      "Epoch 226/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0694 - accuracy: 0.3858 - val_loss: 1.5315 - val_accuracy: 0.0000e+00\n",
      "Epoch 227/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0730 - accuracy: 0.3465 - val_loss: 1.5312 - val_accuracy: 0.0000e+00\n",
      "Epoch 228/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0580 - accuracy: 0.4646 - val_loss: 1.5293 - val_accuracy: 0.0000e+00\n",
      "Epoch 229/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0607 - accuracy: 0.3701 - val_loss: 1.5297 - val_accuracy: 0.0000e+00\n",
      "Epoch 230/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0449 - accuracy: 0.4331 - val_loss: 1.5309 - val_accuracy: 0.0000e+00\n",
      "Epoch 231/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0493 - accuracy: 0.4252 - val_loss: 1.5317 - val_accuracy: 0.0000e+00\n",
      "Epoch 232/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0381 - accuracy: 0.5276 - val_loss: 1.5315 - val_accuracy: 0.0000e+00\n",
      "Epoch 233/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0611 - accuracy: 0.3858 - val_loss: 1.5324 - val_accuracy: 0.0000e+00\n",
      "Epoch 234/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0712 - accuracy: 0.3780 - val_loss: 1.5315 - val_accuracy: 0.0000e+00\n",
      "Epoch 235/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0503 - accuracy: 0.4016 - val_loss: 1.5323 - val_accuracy: 0.0000e+00\n",
      "Epoch 236/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0513 - accuracy: 0.4567 - val_loss: 1.5312 - val_accuracy: 0.0000e+00\n",
      "Epoch 237/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0396 - accuracy: 0.4803 - val_loss: 1.5301 - val_accuracy: 0.0000e+00\n",
      "Epoch 238/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0674 - accuracy: 0.4016 - val_loss: 1.5280 - val_accuracy: 0.0000e+00\n",
      "Epoch 239/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0718 - accuracy: 0.3465 - val_loss: 1.5282 - val_accuracy: 0.0000e+00\n",
      "Epoch 240/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0587 - accuracy: 0.3701 - val_loss: 1.5270 - val_accuracy: 0.0000e+00\n",
      "Epoch 241/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0697 - accuracy: 0.3937 - val_loss: 1.5283 - val_accuracy: 0.0000e+00\n",
      "Epoch 242/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0552 - accuracy: 0.4331 - val_loss: 1.5301 - val_accuracy: 0.0000e+00\n",
      "Epoch 243/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0434 - accuracy: 0.4646 - val_loss: 1.5299 - val_accuracy: 0.0000e+00\n",
      "Epoch 244/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0571 - accuracy: 0.4016 - val_loss: 1.5290 - val_accuracy: 0.0000e+00\n",
      "Epoch 245/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0326 - accuracy: 0.4488 - val_loss: 1.5310 - val_accuracy: 0.0000e+00\n",
      "Epoch 246/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0457 - accuracy: 0.4252 - val_loss: 1.5305 - val_accuracy: 0.0000e+00\n",
      "Epoch 247/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0504 - accuracy: 0.4882 - val_loss: 1.5308 - val_accuracy: 0.0000e+00\n",
      "Epoch 248/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0347 - accuracy: 0.4882 - val_loss: 1.5304 - val_accuracy: 0.0000e+00\n",
      "Epoch 249/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0597 - accuracy: 0.3780 - val_loss: 1.5305 - val_accuracy: 0.0000e+00\n",
      "Epoch 250/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0541 - accuracy: 0.4646 - val_loss: 1.5303 - val_accuracy: 0.0000e+00\n",
      "Epoch 251/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0640 - accuracy: 0.4331 - val_loss: 1.5295 - val_accuracy: 0.0000e+00\n",
      "Epoch 252/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0377 - accuracy: 0.4646 - val_loss: 1.5285 - val_accuracy: 0.0000e+00\n",
      "Epoch 253/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0676 - accuracy: 0.3543 - val_loss: 1.5283 - val_accuracy: 0.0000e+00\n",
      "Epoch 254/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0536 - accuracy: 0.4724 - val_loss: 1.5291 - val_accuracy: 0.0000e+00\n",
      "Epoch 255/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0682 - accuracy: 0.4173 - val_loss: 1.5308 - val_accuracy: 0.0000e+00\n",
      "Epoch 256/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0496 - accuracy: 0.4409 - val_loss: 1.5313 - val_accuracy: 0.0000e+00\n",
      "Epoch 257/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0414 - accuracy: 0.4567 - val_loss: 1.5302 - val_accuracy: 0.0000e+00\n",
      "Epoch 258/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0532 - accuracy: 0.4094 - val_loss: 1.5307 - val_accuracy: 0.0000e+00\n",
      "Epoch 259/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 1.0440 - accuracy: 0.4409 - val_loss: 1.5323 - val_accuracy: 0.0000e+00\n",
      "Epoch 260/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0606 - accuracy: 0.3858 - val_loss: 1.5330 - val_accuracy: 0.0000e+00\n",
      "Epoch 261/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0642 - accuracy: 0.3701 - val_loss: 1.5329 - val_accuracy: 0.0000e+00\n",
      "Epoch 262/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0634 - accuracy: 0.3465 - val_loss: 1.5315 - val_accuracy: 0.0000e+00\n",
      "Epoch 263/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0658 - accuracy: 0.3937 - val_loss: 1.5311 - val_accuracy: 0.0000e+00\n",
      "Epoch 264/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0582 - accuracy: 0.3858 - val_loss: 1.5321 - val_accuracy: 0.0000e+00\n",
      "Epoch 265/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0498 - accuracy: 0.4409 - val_loss: 1.5300 - val_accuracy: 0.0000e+00\n",
      "Epoch 266/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 1.0617 - accuracy: 0.3543 - val_loss: 1.5297 - val_accuracy: 0.0000e+00\n",
      "Epoch 267/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0624 - accuracy: 0.3937 - val_loss: 1.5300 - val_accuracy: 0.0000e+00\n",
      "Epoch 268/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0638 - accuracy: 0.4016 - val_loss: 1.5298 - val_accuracy: 0.0000e+00\n",
      "Epoch 269/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0417 - accuracy: 0.4331 - val_loss: 1.5307 - val_accuracy: 0.0000e+00\n",
      "Epoch 270/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0494 - accuracy: 0.3701 - val_loss: 1.5318 - val_accuracy: 0.0000e+00\n",
      "Epoch 271/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0470 - accuracy: 0.4567 - val_loss: 1.5308 - val_accuracy: 0.0000e+00\n",
      "Epoch 272/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0456 - accuracy: 0.4724 - val_loss: 1.5298 - val_accuracy: 0.0000e+00\n",
      "Epoch 273/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0512 - accuracy: 0.4409 - val_loss: 1.5300 - val_accuracy: 0.0000e+00\n",
      "Epoch 274/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0509 - accuracy: 0.4331 - val_loss: 1.5290 - val_accuracy: 0.0000e+00\n",
      "Epoch 275/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0494 - accuracy: 0.4173 - val_loss: 1.5297 - val_accuracy: 0.0000e+00\n",
      "Epoch 276/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0618 - accuracy: 0.4252 - val_loss: 1.5287 - val_accuracy: 0.0000e+00\n",
      "Epoch 277/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0520 - accuracy: 0.3622 - val_loss: 1.5269 - val_accuracy: 0.0000e+00\n",
      "Epoch 278/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0594 - accuracy: 0.4094 - val_loss: 1.5252 - val_accuracy: 0.0000e+00\n",
      "Epoch 279/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0610 - accuracy: 0.4252 - val_loss: 1.5249 - val_accuracy: 0.0000e+00\n",
      "Epoch 280/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0605 - accuracy: 0.4331 - val_loss: 1.5242 - val_accuracy: 0.0000e+00\n",
      "Epoch 281/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0608 - accuracy: 0.4094 - val_loss: 1.5267 - val_accuracy: 0.0000e+00\n",
      "Epoch 282/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0396 - accuracy: 0.4567 - val_loss: 1.5256 - val_accuracy: 0.0000e+00\n",
      "Epoch 283/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0467 - accuracy: 0.3622 - val_loss: 1.5252 - val_accuracy: 0.0000e+00\n",
      "Epoch 284/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0618 - accuracy: 0.4016 - val_loss: 1.5271 - val_accuracy: 0.0000e+00\n",
      "Epoch 285/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0603 - accuracy: 0.3543 - val_loss: 1.5262 - val_accuracy: 0.0000e+00\n",
      "Epoch 286/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0639 - accuracy: 0.3937 - val_loss: 1.5246 - val_accuracy: 0.0000e+00\n",
      "Epoch 287/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0456 - accuracy: 0.4331 - val_loss: 1.5247 - val_accuracy: 0.0000e+00\n",
      "Epoch 288/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0623 - accuracy: 0.3701 - val_loss: 1.5243 - val_accuracy: 0.0000e+00\n",
      "Epoch 289/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0536 - accuracy: 0.3780 - val_loss: 1.5246 - val_accuracy: 0.0000e+00\n",
      "Epoch 290/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0549 - accuracy: 0.4331 - val_loss: 1.5229 - val_accuracy: 0.0000e+00\n",
      "Epoch 291/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 1.0461 - accuracy: 0.4646 - val_loss: 1.5230 - val_accuracy: 0.0000e+00\n",
      "Epoch 292/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0514 - accuracy: 0.4094 - val_loss: 1.5239 - val_accuracy: 0.0000e+00\n",
      "Epoch 293/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0687 - accuracy: 0.4094 - val_loss: 1.5215 - val_accuracy: 0.0000e+00\n",
      "Epoch 294/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0402 - accuracy: 0.4646 - val_loss: 1.5213 - val_accuracy: 0.0000e+00\n",
      "Epoch 295/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0500 - accuracy: 0.4803 - val_loss: 1.5220 - val_accuracy: 0.0000e+00\n",
      "Epoch 296/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0424 - accuracy: 0.4488 - val_loss: 1.5216 - val_accuracy: 0.0000e+00\n",
      "Epoch 297/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0573 - accuracy: 0.4961 - val_loss: 1.5227 - val_accuracy: 0.0000e+00\n",
      "Epoch 298/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0714 - accuracy: 0.4094 - val_loss: 1.5234 - val_accuracy: 0.0000e+00\n",
      "Epoch 299/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0494 - accuracy: 0.4409 - val_loss: 1.5224 - val_accuracy: 0.0000e+00\n",
      "Epoch 300/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0587 - accuracy: 0.4567 - val_loss: 1.5235 - val_accuracy: 0.0000e+00\n",
      "Epoch 301/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0427 - accuracy: 0.4567 - val_loss: 1.5258 - val_accuracy: 0.0000e+00\n",
      "Epoch 302/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0543 - accuracy: 0.4567 - val_loss: 1.5254 - val_accuracy: 0.0000e+00\n",
      "Epoch 303/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0591 - accuracy: 0.4488 - val_loss: 1.5254 - val_accuracy: 0.0000e+00\n",
      "Epoch 304/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0480 - accuracy: 0.3858 - val_loss: 1.5241 - val_accuracy: 0.0000e+00\n",
      "Epoch 305/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0434 - accuracy: 0.3937 - val_loss: 1.5222 - val_accuracy: 0.0000e+00\n",
      "Epoch 306/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0489 - accuracy: 0.4252 - val_loss: 1.5229 - val_accuracy: 0.0000e+00\n",
      "Epoch 307/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0578 - accuracy: 0.3780 - val_loss: 1.5235 - val_accuracy: 0.0000e+00\n",
      "Epoch 308/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 1.0392 - accuracy: 0.4646 - val_loss: 1.5245 - val_accuracy: 0.0000e+00\n",
      "Epoch 309/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0574 - accuracy: 0.4488 - val_loss: 1.5239 - val_accuracy: 0.0000e+00\n",
      "Epoch 310/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0619 - accuracy: 0.4331 - val_loss: 1.5230 - val_accuracy: 0.0000e+00\n",
      "Epoch 311/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0635 - accuracy: 0.3543 - val_loss: 1.5215 - val_accuracy: 0.0000e+00\n",
      "Epoch 312/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0477 - accuracy: 0.4488 - val_loss: 1.5231 - val_accuracy: 0.0000e+00\n",
      "Epoch 313/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0467 - accuracy: 0.4567 - val_loss: 1.5239 - val_accuracy: 0.0000e+00\n",
      "Epoch 314/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0500 - accuracy: 0.4488 - val_loss: 1.5249 - val_accuracy: 0.0000e+00\n",
      "Epoch 315/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0620 - accuracy: 0.3937 - val_loss: 1.5251 - val_accuracy: 0.0000e+00\n",
      "Epoch 316/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0490 - accuracy: 0.4173 - val_loss: 1.5230 - val_accuracy: 0.0000e+00\n",
      "Epoch 317/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0479 - accuracy: 0.4331 - val_loss: 1.5248 - val_accuracy: 0.0000e+00\n",
      "Epoch 318/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0538 - accuracy: 0.4173 - val_loss: 1.5237 - val_accuracy: 0.0000e+00\n",
      "Epoch 319/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0578 - accuracy: 0.4488 - val_loss: 1.5245 - val_accuracy: 0.0000e+00\n",
      "Epoch 320/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0552 - accuracy: 0.4173 - val_loss: 1.5249 - val_accuracy: 0.0000e+00\n",
      "Epoch 321/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0663 - accuracy: 0.4409 - val_loss: 1.5255 - val_accuracy: 0.0000e+00\n",
      "Epoch 322/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0644 - accuracy: 0.3701 - val_loss: 1.5259 - val_accuracy: 0.0000e+00\n",
      "Epoch 323/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0725 - accuracy: 0.3386 - val_loss: 1.5264 - val_accuracy: 0.0000e+00\n",
      "Epoch 324/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0460 - accuracy: 0.4803 - val_loss: 1.5284 - val_accuracy: 0.0000e+00\n",
      "Epoch 325/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0575 - accuracy: 0.4016 - val_loss: 1.5259 - val_accuracy: 0.0000e+00\n",
      "Epoch 326/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0562 - accuracy: 0.4094 - val_loss: 1.5249 - val_accuracy: 0.0000e+00\n",
      "Epoch 327/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0632 - accuracy: 0.4488 - val_loss: 1.5244 - val_accuracy: 0.0000e+00\n",
      "Epoch 328/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0413 - accuracy: 0.4882 - val_loss: 1.5227 - val_accuracy: 0.0000e+00\n",
      "Epoch 329/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0526 - accuracy: 0.4882 - val_loss: 1.5229 - val_accuracy: 0.0000e+00\n",
      "Epoch 330/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0662 - accuracy: 0.4094 - val_loss: 1.5245 - val_accuracy: 0.0000e+00\n",
      "Epoch 331/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0348 - accuracy: 0.4724 - val_loss: 1.5248 - val_accuracy: 0.0000e+00\n",
      "Epoch 332/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0465 - accuracy: 0.4331 - val_loss: 1.5235 - val_accuracy: 0.0000e+00\n",
      "Epoch 333/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0762 - accuracy: 0.3858 - val_loss: 1.5226 - val_accuracy: 0.0000e+00\n",
      "Epoch 334/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0491 - accuracy: 0.4646 - val_loss: 1.5218 - val_accuracy: 0.0000e+00\n",
      "Epoch 335/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0515 - accuracy: 0.4252 - val_loss: 1.5222 - val_accuracy: 0.0000e+00\n",
      "Epoch 336/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0607 - accuracy: 0.4094 - val_loss: 1.5229 - val_accuracy: 0.0000e+00\n",
      "Epoch 337/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0542 - accuracy: 0.4094 - val_loss: 1.5217 - val_accuracy: 0.0000e+00\n",
      "Epoch 338/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0517 - accuracy: 0.4409 - val_loss: 1.5208 - val_accuracy: 0.0000e+00\n",
      "Epoch 339/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0525 - accuracy: 0.4724 - val_loss: 1.5205 - val_accuracy: 0.0000e+00\n",
      "Epoch 340/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0656 - accuracy: 0.4094 - val_loss: 1.5223 - val_accuracy: 0.0000e+00\n",
      "Epoch 341/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0494 - accuracy: 0.4173 - val_loss: 1.5217 - val_accuracy: 0.0000e+00\n",
      "Epoch 342/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0538 - accuracy: 0.3937 - val_loss: 1.5216 - val_accuracy: 0.0000e+00\n",
      "Epoch 343/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0556 - accuracy: 0.3937 - val_loss: 1.5199 - val_accuracy: 0.0000e+00\n",
      "Epoch 344/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0422 - accuracy: 0.4173 - val_loss: 1.5192 - val_accuracy: 0.0000e+00\n",
      "Epoch 345/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0608 - accuracy: 0.4094 - val_loss: 1.5212 - val_accuracy: 0.0000e+00\n",
      "Epoch 346/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0319 - accuracy: 0.4331 - val_loss: 1.5199 - val_accuracy: 0.0000e+00\n",
      "Epoch 347/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0467 - accuracy: 0.5039 - val_loss: 1.5192 - val_accuracy: 0.0000e+00\n",
      "Epoch 348/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0671 - accuracy: 0.4094 - val_loss: 1.5193 - val_accuracy: 0.0000e+00\n",
      "Epoch 349/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 1.0612 - accuracy: 0.4016 - val_loss: 1.5200 - val_accuracy: 0.0000e+00\n",
      "Epoch 350/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0558 - accuracy: 0.4409 - val_loss: 1.5197 - val_accuracy: 0.0000e+00\n",
      "Epoch 351/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0496 - accuracy: 0.4488 - val_loss: 1.5206 - val_accuracy: 0.0000e+00\n",
      "Epoch 352/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0690 - accuracy: 0.3622 - val_loss: 1.5186 - val_accuracy: 0.0000e+00\n",
      "Epoch 353/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0489 - accuracy: 0.4252 - val_loss: 1.5195 - val_accuracy: 0.0000e+00\n",
      "Epoch 354/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0537 - accuracy: 0.3937 - val_loss: 1.5204 - val_accuracy: 0.0000e+00\n",
      "Epoch 355/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0620 - accuracy: 0.3858 - val_loss: 1.5211 - val_accuracy: 0.0000e+00\n",
      "Epoch 356/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0505 - accuracy: 0.4409 - val_loss: 1.5202 - val_accuracy: 0.0000e+00\n",
      "Epoch 357/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0400 - accuracy: 0.5039 - val_loss: 1.5205 - val_accuracy: 0.0000e+00\n",
      "Epoch 358/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0500 - accuracy: 0.4409 - val_loss: 1.5188 - val_accuracy: 0.0000e+00\n",
      "Epoch 359/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0535 - accuracy: 0.4488 - val_loss: 1.5185 - val_accuracy: 0.0000e+00\n",
      "Epoch 360/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0585 - accuracy: 0.4252 - val_loss: 1.5194 - val_accuracy: 0.0000e+00\n",
      "Epoch 361/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0451 - accuracy: 0.4567 - val_loss: 1.5206 - val_accuracy: 0.0000e+00\n",
      "Epoch 362/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0557 - accuracy: 0.4173 - val_loss: 1.5190 - val_accuracy: 0.0000e+00\n",
      "Epoch 363/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0388 - accuracy: 0.4646 - val_loss: 1.5182 - val_accuracy: 0.0000e+00\n",
      "Epoch 364/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0528 - accuracy: 0.3937 - val_loss: 1.5199 - val_accuracy: 0.0000e+00\n",
      "Epoch 365/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0677 - accuracy: 0.4252 - val_loss: 1.5200 - val_accuracy: 0.0000e+00\n",
      "Epoch 366/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0455 - accuracy: 0.4094 - val_loss: 1.5184 - val_accuracy: 0.0000e+00\n",
      "Epoch 367/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0396 - accuracy: 0.4803 - val_loss: 1.5167 - val_accuracy: 0.0000e+00\n",
      "Epoch 368/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0297 - accuracy: 0.4488 - val_loss: 1.5173 - val_accuracy: 0.0000e+00\n",
      "Epoch 369/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0503 - accuracy: 0.4409 - val_loss: 1.5192 - val_accuracy: 0.0000e+00\n",
      "Epoch 370/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0476 - accuracy: 0.5039 - val_loss: 1.5180 - val_accuracy: 0.0000e+00\n",
      "Epoch 371/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0607 - accuracy: 0.3780 - val_loss: 1.5171 - val_accuracy: 0.0000e+00\n",
      "Epoch 372/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0674 - accuracy: 0.3543 - val_loss: 1.5178 - val_accuracy: 0.0000e+00\n",
      "Epoch 373/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0558 - accuracy: 0.4331 - val_loss: 1.5179 - val_accuracy: 0.0000e+00\n",
      "Epoch 374/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0413 - accuracy: 0.4331 - val_loss: 1.5196 - val_accuracy: 0.0000e+00\n",
      "Epoch 375/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0607 - accuracy: 0.3701 - val_loss: 1.5194 - val_accuracy: 0.0000e+00\n",
      "Epoch 376/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0506 - accuracy: 0.4567 - val_loss: 1.5184 - val_accuracy: 0.0000e+00\n",
      "Epoch 377/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0446 - accuracy: 0.4724 - val_loss: 1.5201 - val_accuracy: 0.0000e+00\n",
      "Epoch 378/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0434 - accuracy: 0.4331 - val_loss: 1.5205 - val_accuracy: 0.0000e+00\n",
      "Epoch 379/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0524 - accuracy: 0.4567 - val_loss: 1.5214 - val_accuracy: 0.0000e+00\n",
      "Epoch 380/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0494 - accuracy: 0.3780 - val_loss: 1.5219 - val_accuracy: 0.0000e+00\n",
      "Epoch 381/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0495 - accuracy: 0.4016 - val_loss: 1.5232 - val_accuracy: 0.0000e+00\n",
      "Epoch 382/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0607 - accuracy: 0.4646 - val_loss: 1.5222 - val_accuracy: 0.0000e+00\n",
      "Epoch 383/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0441 - accuracy: 0.5276 - val_loss: 1.5200 - val_accuracy: 0.0000e+00\n",
      "Epoch 384/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0597 - accuracy: 0.4173 - val_loss: 1.5200 - val_accuracy: 0.0000e+00\n",
      "Epoch 385/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0586 - accuracy: 0.4252 - val_loss: 1.5173 - val_accuracy: 0.0000e+00\n",
      "Epoch 386/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0409 - accuracy: 0.5197 - val_loss: 1.5179 - val_accuracy: 0.0000e+00\n",
      "Epoch 387/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0619 - accuracy: 0.3937 - val_loss: 1.5175 - val_accuracy: 0.0000e+00\n",
      "Epoch 388/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0498 - accuracy: 0.4567 - val_loss: 1.5180 - val_accuracy: 0.0000e+00\n",
      "Epoch 389/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0426 - accuracy: 0.4252 - val_loss: 1.5174 - val_accuracy: 0.0000e+00\n",
      "Epoch 390/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0486 - accuracy: 0.4094 - val_loss: 1.5171 - val_accuracy: 0.0000e+00\n",
      "Epoch 391/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 1.0620 - accuracy: 0.4173 - val_loss: 1.5169 - val_accuracy: 0.0000e+00\n",
      "Epoch 392/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0407 - accuracy: 0.4488 - val_loss: 1.5171 - val_accuracy: 0.0000e+00\n",
      "Epoch 393/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0481 - accuracy: 0.4173 - val_loss: 1.5168 - val_accuracy: 0.0000e+00\n",
      "Epoch 394/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0287 - accuracy: 0.4409 - val_loss: 1.5167 - val_accuracy: 0.0000e+00\n",
      "Epoch 395/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0449 - accuracy: 0.4173 - val_loss: 1.5162 - val_accuracy: 0.0000e+00\n",
      "Epoch 396/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0651 - accuracy: 0.4016 - val_loss: 1.5139 - val_accuracy: 0.0000e+00\n",
      "Epoch 397/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0331 - accuracy: 0.5276 - val_loss: 1.5128 - val_accuracy: 0.0000e+00\n",
      "Epoch 398/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0699 - accuracy: 0.3780 - val_loss: 1.5147 - val_accuracy: 0.0000e+00\n",
      "Epoch 399/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0343 - accuracy: 0.4409 - val_loss: 1.5140 - val_accuracy: 0.0000e+00\n",
      "Epoch 400/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0395 - accuracy: 0.4488 - val_loss: 1.5132 - val_accuracy: 0.0000e+00\n",
      "Epoch 401/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0497 - accuracy: 0.4567 - val_loss: 1.5133 - val_accuracy: 0.0000e+00\n",
      "Epoch 402/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0504 - accuracy: 0.4252 - val_loss: 1.5145 - val_accuracy: 0.0000e+00\n",
      "Epoch 403/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0514 - accuracy: 0.4646 - val_loss: 1.5143 - val_accuracy: 0.0000e+00\n",
      "Epoch 404/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0407 - accuracy: 0.4646 - val_loss: 1.5154 - val_accuracy: 0.0000e+00\n",
      "Epoch 405/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0623 - accuracy: 0.3937 - val_loss: 1.5150 - val_accuracy: 0.0000e+00\n",
      "Epoch 406/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0319 - accuracy: 0.4803 - val_loss: 1.5160 - val_accuracy: 0.0000e+00\n",
      "Epoch 407/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0570 - accuracy: 0.4488 - val_loss: 1.5158 - val_accuracy: 0.0000e+00\n",
      "Epoch 408/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0315 - accuracy: 0.5039 - val_loss: 1.5157 - val_accuracy: 0.0000e+00\n",
      "Epoch 409/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0449 - accuracy: 0.5039 - val_loss: 1.5145 - val_accuracy: 0.0000e+00\n",
      "Epoch 410/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0713 - accuracy: 0.3701 - val_loss: 1.5161 - val_accuracy: 0.0000e+00\n",
      "Epoch 411/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0515 - accuracy: 0.4567 - val_loss: 1.5151 - val_accuracy: 0.0000e+00\n",
      "Epoch 412/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0600 - accuracy: 0.4331 - val_loss: 1.5148 - val_accuracy: 0.0000e+00\n",
      "Epoch 413/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0627 - accuracy: 0.4252 - val_loss: 1.5154 - val_accuracy: 0.0000e+00\n",
      "Epoch 414/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0358 - accuracy: 0.5118 - val_loss: 1.5151 - val_accuracy: 0.0000e+00\n",
      "Epoch 415/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0464 - accuracy: 0.4961 - val_loss: 1.5155 - val_accuracy: 0.0000e+00\n",
      "Epoch 416/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0501 - accuracy: 0.4882 - val_loss: 1.5153 - val_accuracy: 0.0000e+00\n",
      "Epoch 417/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0482 - accuracy: 0.3937 - val_loss: 1.5151 - val_accuracy: 0.0000e+00\n",
      "Epoch 418/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0639 - accuracy: 0.3701 - val_loss: 1.5134 - val_accuracy: 0.0000e+00\n",
      "Epoch 419/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0493 - accuracy: 0.4173 - val_loss: 1.5132 - val_accuracy: 0.0000e+00\n",
      "Epoch 420/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0485 - accuracy: 0.5354 - val_loss: 1.5115 - val_accuracy: 0.0000e+00\n",
      "Epoch 421/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0587 - accuracy: 0.4252 - val_loss: 1.5141 - val_accuracy: 0.0000e+00\n",
      "Epoch 422/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0339 - accuracy: 0.4882 - val_loss: 1.5145 - val_accuracy: 0.0000e+00\n",
      "Epoch 423/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0414 - accuracy: 0.4882 - val_loss: 1.5133 - val_accuracy: 0.0000e+00\n",
      "Epoch 424/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0229 - accuracy: 0.5118 - val_loss: 1.5127 - val_accuracy: 0.0000e+00\n",
      "Epoch 425/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0389 - accuracy: 0.4803 - val_loss: 1.5112 - val_accuracy: 0.0000e+00\n",
      "Epoch 426/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0419 - accuracy: 0.4803 - val_loss: 1.5121 - val_accuracy: 0.0000e+00\n",
      "Epoch 427/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0485 - accuracy: 0.4331 - val_loss: 1.5129 - val_accuracy: 0.0000e+00\n",
      "Epoch 428/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0416 - accuracy: 0.3858 - val_loss: 1.5146 - val_accuracy: 0.0000e+00\n",
      "Epoch 429/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0307 - accuracy: 0.4724 - val_loss: 1.5134 - val_accuracy: 0.0000e+00\n",
      "Epoch 430/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0455 - accuracy: 0.4409 - val_loss: 1.5145 - val_accuracy: 0.0000e+00\n",
      "Epoch 431/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0521 - accuracy: 0.4724 - val_loss: 1.5145 - val_accuracy: 0.0000e+00\n",
      "Epoch 432/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0391 - accuracy: 0.4488 - val_loss: 1.5134 - val_accuracy: 0.0000e+00\n",
      "Epoch 433/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0528 - accuracy: 0.4488 - val_loss: 1.5140 - val_accuracy: 0.0000e+00\n",
      "Epoch 434/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0421 - accuracy: 0.4803 - val_loss: 1.5137 - val_accuracy: 0.0000e+00\n",
      "Epoch 435/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0482 - accuracy: 0.4252 - val_loss: 1.5127 - val_accuracy: 0.0000e+00\n",
      "Epoch 436/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0535 - accuracy: 0.4016 - val_loss: 1.5140 - val_accuracy: 0.0000e+00\n",
      "Epoch 437/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0319 - accuracy: 0.4803 - val_loss: 1.5124 - val_accuracy: 0.0000e+00\n",
      "Epoch 438/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0629 - accuracy: 0.4094 - val_loss: 1.5112 - val_accuracy: 0.0000e+00\n",
      "Epoch 439/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0400 - accuracy: 0.4094 - val_loss: 1.5121 - val_accuracy: 0.0000e+00\n",
      "Epoch 440/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0417 - accuracy: 0.4803 - val_loss: 1.5145 - val_accuracy: 0.0000e+00\n",
      "Epoch 441/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0486 - accuracy: 0.4724 - val_loss: 1.5132 - val_accuracy: 0.0000e+00\n",
      "Epoch 442/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0405 - accuracy: 0.4252 - val_loss: 1.5127 - val_accuracy: 0.0000e+00\n",
      "Epoch 443/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0465 - accuracy: 0.4882 - val_loss: 1.5141 - val_accuracy: 0.0000e+00\n",
      "Epoch 444/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0544 - accuracy: 0.4488 - val_loss: 1.5145 - val_accuracy: 0.0000e+00\n",
      "Epoch 445/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0573 - accuracy: 0.4094 - val_loss: 1.5143 - val_accuracy: 0.0000e+00\n",
      "Epoch 446/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0480 - accuracy: 0.4016 - val_loss: 1.5119 - val_accuracy: 0.0000e+00\n",
      "Epoch 447/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0618 - accuracy: 0.4331 - val_loss: 1.5117 - val_accuracy: 0.0000e+00\n",
      "Epoch 448/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0433 - accuracy: 0.4173 - val_loss: 1.5117 - val_accuracy: 0.0000e+00\n",
      "Epoch 449/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0557 - accuracy: 0.4488 - val_loss: 1.5111 - val_accuracy: 0.0000e+00\n",
      "Epoch 450/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0550 - accuracy: 0.4567 - val_loss: 1.5119 - val_accuracy: 0.0000e+00\n",
      "Epoch 451/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0359 - accuracy: 0.4724 - val_loss: 1.5134 - val_accuracy: 0.0000e+00\n",
      "Epoch 452/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0473 - accuracy: 0.4173 - val_loss: 1.5135 - val_accuracy: 0.0000e+00\n",
      "Epoch 453/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0591 - accuracy: 0.3858 - val_loss: 1.5138 - val_accuracy: 0.0000e+00\n",
      "Epoch 454/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0611 - accuracy: 0.4094 - val_loss: 1.5134 - val_accuracy: 0.0000e+00\n",
      "Epoch 455/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0258 - accuracy: 0.4567 - val_loss: 1.5143 - val_accuracy: 0.0000e+00\n",
      "Epoch 456/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0407 - accuracy: 0.4252 - val_loss: 1.5146 - val_accuracy: 0.0000e+00\n",
      "Epoch 457/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0417 - accuracy: 0.4646 - val_loss: 1.5142 - val_accuracy: 0.0000e+00\n",
      "Epoch 458/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0558 - accuracy: 0.4252 - val_loss: 1.5167 - val_accuracy: 0.0000e+00\n",
      "Epoch 459/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0378 - accuracy: 0.4409 - val_loss: 1.5169 - val_accuracy: 0.0000e+00\n",
      "Epoch 460/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0570 - accuracy: 0.4094 - val_loss: 1.5161 - val_accuracy: 0.0000e+00\n",
      "Epoch 461/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0525 - accuracy: 0.4173 - val_loss: 1.5140 - val_accuracy: 0.0000e+00\n",
      "Epoch 462/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0732 - accuracy: 0.3543 - val_loss: 1.5114 - val_accuracy: 0.0000e+00\n",
      "Epoch 463/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0472 - accuracy: 0.4488 - val_loss: 1.5083 - val_accuracy: 0.0000e+00\n",
      "Epoch 464/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0452 - accuracy: 0.4646 - val_loss: 1.5082 - val_accuracy: 0.0000e+00\n",
      "Epoch 465/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0371 - accuracy: 0.4961 - val_loss: 1.5080 - val_accuracy: 0.0000e+00\n",
      "Epoch 466/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0674 - accuracy: 0.3622 - val_loss: 1.5093 - val_accuracy: 0.0000e+00\n",
      "Epoch 467/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0468 - accuracy: 0.4803 - val_loss: 1.5093 - val_accuracy: 0.0000e+00\n",
      "Epoch 468/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0490 - accuracy: 0.4252 - val_loss: 1.5099 - val_accuracy: 0.0000e+00\n",
      "Epoch 469/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0590 - accuracy: 0.4567 - val_loss: 1.5113 - val_accuracy: 0.0000e+00\n",
      "Epoch 470/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0260 - accuracy: 0.5748 - val_loss: 1.5104 - val_accuracy: 0.0000e+00\n",
      "Epoch 471/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0497 - accuracy: 0.3465 - val_loss: 1.5093 - val_accuracy: 0.0000e+00\n",
      "Epoch 472/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0362 - accuracy: 0.4409 - val_loss: 1.5093 - val_accuracy: 0.0000e+00\n",
      "Epoch 473/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0432 - accuracy: 0.4488 - val_loss: 1.5092 - val_accuracy: 0.0000e+00\n",
      "Epoch 474/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0460 - accuracy: 0.4646 - val_loss: 1.5092 - val_accuracy: 0.0000e+00\n",
      "Epoch 475/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0621 - accuracy: 0.3622 - val_loss: 1.5093 - val_accuracy: 0.0000e+00\n",
      "Epoch 476/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0357 - accuracy: 0.4961 - val_loss: 1.5102 - val_accuracy: 0.0000e+00\n",
      "Epoch 477/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0376 - accuracy: 0.4252 - val_loss: 1.5094 - val_accuracy: 0.0000e+00\n",
      "Epoch 478/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0376 - accuracy: 0.4252 - val_loss: 1.5076 - val_accuracy: 0.0000e+00\n",
      "Epoch 479/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0530 - accuracy: 0.4646 - val_loss: 1.5086 - val_accuracy: 0.0000e+00\n",
      "Epoch 480/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0450 - accuracy: 0.4409 - val_loss: 1.5069 - val_accuracy: 0.0000e+00\n",
      "Epoch 481/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0584 - accuracy: 0.3937 - val_loss: 1.5078 - val_accuracy: 0.0000e+00\n",
      "Epoch 482/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0437 - accuracy: 0.4488 - val_loss: 1.5062 - val_accuracy: 0.0000e+00\n",
      "Epoch 483/500\n",
      "127/127 [==============================] - 0s 86us/step - loss: 1.0441 - accuracy: 0.4646 - val_loss: 1.5067 - val_accuracy: 0.0000e+00\n",
      "Epoch 484/500\n",
      "127/127 [==============================] - 0s 79us/step - loss: 1.0437 - accuracy: 0.4567 - val_loss: 1.5077 - val_accuracy: 0.0000e+00\n",
      "Epoch 485/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0364 - accuracy: 0.4331 - val_loss: 1.5075 - val_accuracy: 0.0000e+00\n",
      "Epoch 486/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0426 - accuracy: 0.5039 - val_loss: 1.5066 - val_accuracy: 0.0000e+00\n",
      "Epoch 487/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0478 - accuracy: 0.4488 - val_loss: 1.5065 - val_accuracy: 0.0000e+00\n",
      "Epoch 488/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0356 - accuracy: 0.5118 - val_loss: 1.5074 - val_accuracy: 0.0000e+00\n",
      "Epoch 489/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0389 - accuracy: 0.4567 - val_loss: 1.5077 - val_accuracy: 0.0000e+00\n",
      "Epoch 490/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0545 - accuracy: 0.4094 - val_loss: 1.5065 - val_accuracy: 0.0000e+00\n",
      "Epoch 491/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0458 - accuracy: 0.4331 - val_loss: 1.5073 - val_accuracy: 0.0000e+00\n",
      "Epoch 492/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0433 - accuracy: 0.4094 - val_loss: 1.5065 - val_accuracy: 0.0000e+00\n",
      "Epoch 493/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0360 - accuracy: 0.3937 - val_loss: 1.5089 - val_accuracy: 0.0000e+00\n",
      "Epoch 494/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0469 - accuracy: 0.4567 - val_loss: 1.5091 - val_accuracy: 0.0000e+00\n",
      "Epoch 495/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0313 - accuracy: 0.4488 - val_loss: 1.5091 - val_accuracy: 0.0000e+00\n",
      "Epoch 496/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0507 - accuracy: 0.4331 - val_loss: 1.5089 - val_accuracy: 0.0000e+00\n",
      "Epoch 497/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0544 - accuracy: 0.4409 - val_loss: 1.5075 - val_accuracy: 0.0000e+00\n",
      "Epoch 498/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0484 - accuracy: 0.3858 - val_loss: 1.5073 - val_accuracy: 0.0000e+00\n",
      "Epoch 499/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0502 - accuracy: 0.4803 - val_loss: 1.5063 - val_accuracy: 0.0000e+00\n",
      "Epoch 500/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0346 - accuracy: 0.4409 - val_loss: 1.5056 - val_accuracy: 0.0000e+00\n",
      "Test accuracy : 61.33%\n",
      "Test accuracy : 61.33%\n"
     ]
    }
   ],
   "source": [
    "#改变神经元个数\n",
    "#SGD\n",
    "model2 = Sequential()\n",
    "model2.add(Dense(20,input_dim=4,activation='sigmoid'))\n",
    "# 隐层\n",
    "model2.add(Dense(30, activation='sigmoid',input_dim=20))  # Dense层为中间层\n",
    "model2.add(Dropout(0.5))\n",
    "model2.add(Dense(20, activation='sigmoid',input_dim=20))  # Dense层为中间层\n",
    "\n",
    "# 输出层\n",
    "model2.add(Dense(3, input_dim=20,activation='softmax'))\n",
    "sgd=optimizers.SGD(learning_rate=0.01)\n",
    "model2.compile(loss='categorical_crossentropy', optimizer=sgd,metrics=['accuracy'])\n",
    "# model2.summary()\n",
    "historysgd=model2.fit(input_data,correct_data,validation_split=0.15,epochs=500)\n",
    "# ans=model2.predict(input_test)\n",
    "score3=model2.evaluate(input_test,correct_test,verbose=0)\n",
    "print(\"Test accuracy : %.2f%%\" %(score3[1]*100))\n",
    "# score3=model2.evaluate(input_test,correct_test,verbose=0)\n",
    "# print(\"Test accuracy : %.2f%%\" %(score3[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 127 samples, validate on 23 samples\n",
      "Epoch 1/500\n",
      "127/127 [==============================] - 0s 526us/step - loss: 1.5599 - accuracy: 0.2126 - val_loss: 0.4081 - val_accuracy: 1.0000\n",
      "Epoch 2/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.4576 - accuracy: 0.2126 - val_loss: 0.4833 - val_accuracy: 1.0000\n",
      "Epoch 3/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.3738 - accuracy: 0.2126 - val_loss: 0.5594 - val_accuracy: 1.0000\n",
      "Epoch 4/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.3080 - accuracy: 0.2126 - val_loss: 0.6334 - val_accuracy: 1.0000\n",
      "Epoch 5/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.2571 - accuracy: 0.2126 - val_loss: 0.7053 - val_accuracy: 1.0000\n",
      "Epoch 6/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.2164 - accuracy: 0.2126 - val_loss: 0.7733 - val_accuracy: 1.0000\n",
      "Epoch 7/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.1849 - accuracy: 0.2126 - val_loss: 0.8363 - val_accuracy: 1.0000\n",
      "Epoch 8/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.1608 - accuracy: 0.2126 - val_loss: 0.8949 - val_accuracy: 1.0000\n",
      "Epoch 9/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.1410 - accuracy: 0.2126 - val_loss: 0.9485 - val_accuracy: 1.0000\n",
      "Epoch 10/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.1263 - accuracy: 0.2126 - val_loss: 0.9982 - val_accuracy: 1.0000\n",
      "Epoch 11/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.1143 - accuracy: 0.2126 - val_loss: 1.0430 - val_accuracy: 1.0000\n",
      "Epoch 12/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.1039 - accuracy: 0.2126 - val_loss: 1.0851 - val_accuracy: 0.2174\n",
      "Epoch 13/500\n",
      "127/127 [==============================] - 0s 48us/step - loss: 1.0963 - accuracy: 0.3858 - val_loss: 1.1236 - val_accuracy: 0.0000e+00\n",
      "Epoch 14/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0899 - accuracy: 0.3937 - val_loss: 1.1588 - val_accuracy: 0.0000e+00\n",
      "Epoch 15/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0854 - accuracy: 0.3937 - val_loss: 1.1904 - val_accuracy: 0.0000e+00\n",
      "Epoch 16/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0815 - accuracy: 0.3937 - val_loss: 1.2186 - val_accuracy: 0.0000e+00\n",
      "Epoch 17/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0776 - accuracy: 0.3937 - val_loss: 1.2457 - val_accuracy: 0.0000e+00\n",
      "Epoch 18/500\n",
      "127/127 [==============================] - 0s 110us/step - loss: 1.0746 - accuracy: 0.3937 - val_loss: 1.2702 - val_accuracy: 0.0000e+00\n",
      "Epoch 19/500\n",
      "127/127 [==============================] - 0s 79us/step - loss: 1.0728 - accuracy: 0.5433 - val_loss: 1.2930 - val_accuracy: 0.0000e+00\n",
      "Epoch 20/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0703 - accuracy: 0.3937 - val_loss: 1.3142 - val_accuracy: 0.0000e+00\n",
      "Epoch 21/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0699 - accuracy: 0.2992 - val_loss: 1.3320 - val_accuracy: 0.0000e+00\n",
      "Epoch 22/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0674 - accuracy: 0.3937 - val_loss: 1.3494 - val_accuracy: 0.0000e+00\n",
      "Epoch 23/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0663 - accuracy: 0.3937 - val_loss: 1.3648 - val_accuracy: 0.0000e+00\n",
      "Epoch 24/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0657 - accuracy: 0.5433 - val_loss: 1.3789 - val_accuracy: 0.0000e+00\n",
      "Epoch 25/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0655 - accuracy: 0.5197 - val_loss: 1.3914 - val_accuracy: 0.0000e+00\n",
      "Epoch 26/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0644 - accuracy: 0.4882 - val_loss: 1.4042 - val_accuracy: 0.0000e+00\n",
      "Epoch 27/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0636 - accuracy: 0.6063 - val_loss: 1.4149 - val_accuracy: 0.0000e+00\n",
      "Epoch 28/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0637 - accuracy: 0.4016 - val_loss: 1.4246 - val_accuracy: 0.0000e+00\n",
      "Epoch 29/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0633 - accuracy: 0.4173 - val_loss: 1.4345 - val_accuracy: 0.0000e+00\n",
      "Epoch 30/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0634 - accuracy: 0.4882 - val_loss: 1.4437 - val_accuracy: 0.0000e+00\n",
      "Epoch 31/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0625 - accuracy: 0.4724 - val_loss: 1.4515 - val_accuracy: 0.0000e+00\n",
      "Epoch 32/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0621 - accuracy: 0.5197 - val_loss: 1.4590 - val_accuracy: 0.0000e+00\n",
      "Epoch 33/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0620 - accuracy: 0.4803 - val_loss: 1.4657 - val_accuracy: 0.0000e+00\n",
      "Epoch 34/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0618 - accuracy: 0.4724 - val_loss: 1.4722 - val_accuracy: 0.0000e+00\n",
      "Epoch 35/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0615 - accuracy: 0.4094 - val_loss: 1.4772 - val_accuracy: 0.0000e+00\n",
      "Epoch 36/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0613 - accuracy: 0.5984 - val_loss: 1.4824 - val_accuracy: 0.0000e+00\n",
      "Epoch 37/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0612 - accuracy: 0.5669 - val_loss: 1.4879 - val_accuracy: 0.0000e+00\n",
      "Epoch 38/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0607 - accuracy: 0.6299 - val_loss: 1.4922 - val_accuracy: 0.0000e+00\n",
      "Epoch 39/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0608 - accuracy: 0.6535 - val_loss: 1.4965 - val_accuracy: 0.0000e+00\n",
      "Epoch 40/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0607 - accuracy: 0.4724 - val_loss: 1.5002 - val_accuracy: 0.0000e+00\n",
      "Epoch 41/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0604 - accuracy: 0.7244 - val_loss: 1.5036 - val_accuracy: 0.0000e+00\n",
      "Epoch 42/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0604 - accuracy: 0.5591 - val_loss: 1.5068 - val_accuracy: 0.0000e+00\n",
      "Epoch 43/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0618 - accuracy: 0.6535 - val_loss: 1.5092 - val_accuracy: 0.0000e+00\n",
      "Epoch 44/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0603 - accuracy: 0.6850 - val_loss: 1.5117 - val_accuracy: 0.0000e+00\n",
      "Epoch 45/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0611 - accuracy: 0.5669 - val_loss: 1.5139 - val_accuracy: 0.0000e+00\n",
      "Epoch 46/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0603 - accuracy: 0.6614 - val_loss: 1.5162 - val_accuracy: 0.0000e+00\n",
      "Epoch 47/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0607 - accuracy: 0.4724 - val_loss: 1.5184 - val_accuracy: 0.0000e+00\n",
      "Epoch 48/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0602 - accuracy: 0.7795 - val_loss: 1.5199 - val_accuracy: 0.0000e+00\n",
      "Epoch 49/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0605 - accuracy: 0.5197 - val_loss: 1.5217 - val_accuracy: 0.0000e+00\n",
      "Epoch 50/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0599 - accuracy: 0.6220 - val_loss: 1.5238 - val_accuracy: 0.0000e+00\n",
      "Epoch 51/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0599 - accuracy: 0.6772 - val_loss: 1.5255 - val_accuracy: 0.0000e+00\n",
      "Epoch 52/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0600 - accuracy: 0.5276 - val_loss: 1.5266 - val_accuracy: 0.0000e+00\n",
      "Epoch 53/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0607 - accuracy: 0.4882 - val_loss: 1.5285 - val_accuracy: 0.0000e+00\n",
      "Epoch 54/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0598 - accuracy: 0.7087 - val_loss: 1.5295 - val_accuracy: 0.0000e+00\n",
      "Epoch 55/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0603 - accuracy: 0.4567 - val_loss: 1.5303 - val_accuracy: 0.0000e+00\n",
      "Epoch 56/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0601 - accuracy: 0.6693 - val_loss: 1.5310 - val_accuracy: 0.0000e+00\n",
      "Epoch 57/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0601 - accuracy: 0.7244 - val_loss: 1.5321 - val_accuracy: 0.0000e+00\n",
      "Epoch 58/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0609 - accuracy: 0.5748 - val_loss: 1.5326 - val_accuracy: 0.0000e+00\n",
      "Epoch 59/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0595 - accuracy: 0.6772 - val_loss: 1.5329 - val_accuracy: 0.0000e+00\n",
      "Epoch 60/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0605 - accuracy: 0.6772 - val_loss: 1.5342 - val_accuracy: 0.0000e+00\n",
      "Epoch 61/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0600 - accuracy: 0.6614 - val_loss: 1.5353 - val_accuracy: 0.0000e+00\n",
      "Epoch 62/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0606 - accuracy: 0.5433 - val_loss: 1.5356 - val_accuracy: 0.0000e+00\n",
      "Epoch 63/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0595 - accuracy: 0.6535 - val_loss: 1.5362 - val_accuracy: 0.0000e+00\n",
      "Epoch 64/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0601 - accuracy: 0.4803 - val_loss: 1.5367 - val_accuracy: 0.0000e+00\n",
      "Epoch 65/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0604 - accuracy: 0.5591 - val_loss: 1.5375 - val_accuracy: 0.0000e+00\n",
      "Epoch 66/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0596 - accuracy: 0.6220 - val_loss: 1.5380 - val_accuracy: 0.0000e+00\n",
      "Epoch 67/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0595 - accuracy: 0.5039 - val_loss: 1.5383 - val_accuracy: 0.0000e+00\n",
      "Epoch 68/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0594 - accuracy: 0.6535 - val_loss: 1.5385 - val_accuracy: 0.0000e+00\n",
      "Epoch 69/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0595 - accuracy: 0.7008 - val_loss: 1.5388 - val_accuracy: 0.0000e+00\n",
      "Epoch 70/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0593 - accuracy: 0.4803 - val_loss: 1.5389 - val_accuracy: 0.0000e+00\n",
      "Epoch 71/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0592 - accuracy: 0.5512 - val_loss: 1.5391 - val_accuracy: 0.0000e+00\n",
      "Epoch 72/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0591 - accuracy: 0.7638 - val_loss: 1.5389 - val_accuracy: 0.0000e+00\n",
      "Epoch 73/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0592 - accuracy: 0.6535 - val_loss: 1.5391 - val_accuracy: 0.0000e+00\n",
      "Epoch 74/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0594 - accuracy: 0.5669 - val_loss: 1.5391 - val_accuracy: 0.0000e+00\n",
      "Epoch 75/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0590 - accuracy: 0.6772 - val_loss: 1.5385 - val_accuracy: 0.0000e+00\n",
      "Epoch 76/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0586 - accuracy: 0.7795 - val_loss: 1.5388 - val_accuracy: 0.0000e+00\n",
      "Epoch 77/500\n",
      "127/127 [==============================] - 0s 24us/step - loss: 1.0596 - accuracy: 0.6299 - val_loss: 1.5384 - val_accuracy: 0.0000e+00\n",
      "Epoch 78/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0593 - accuracy: 0.4803 - val_loss: 1.5393 - val_accuracy: 0.0000e+00\n",
      "Epoch 79/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0601 - accuracy: 0.4882 - val_loss: 1.5393 - val_accuracy: 0.0000e+00\n",
      "Epoch 80/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0600 - accuracy: 0.5276 - val_loss: 1.5402 - val_accuracy: 0.0000e+00\n",
      "Epoch 81/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0590 - accuracy: 0.7402 - val_loss: 1.5409 - val_accuracy: 0.0000e+00\n",
      "Epoch 82/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0592 - accuracy: 0.6457 - val_loss: 1.5406 - val_accuracy: 0.0000e+00\n",
      "Epoch 83/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0589 - accuracy: 0.6535 - val_loss: 1.5410 - val_accuracy: 0.0000e+00\n",
      "Epoch 84/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0589 - accuracy: 0.7244 - val_loss: 1.5419 - val_accuracy: 0.0000e+00\n",
      "Epoch 85/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0588 - accuracy: 0.6693 - val_loss: 1.5412 - val_accuracy: 0.0000e+00\n",
      "Epoch 86/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0585 - accuracy: 0.7165 - val_loss: 1.5406 - val_accuracy: 0.0000e+00\n",
      "Epoch 87/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0585 - accuracy: 0.7165 - val_loss: 1.5401 - val_accuracy: 0.0000e+00\n",
      "Epoch 88/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0599 - accuracy: 0.6299 - val_loss: 1.5398 - val_accuracy: 0.0000e+00\n",
      "Epoch 89/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0582 - accuracy: 0.6929 - val_loss: 1.5396 - val_accuracy: 0.0000e+00\n",
      "Epoch 90/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0585 - accuracy: 0.5827 - val_loss: 1.5397 - val_accuracy: 0.0000e+00\n",
      "Epoch 91/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0590 - accuracy: 0.5197 - val_loss: 1.5403 - val_accuracy: 0.0000e+00\n",
      "Epoch 92/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0590 - accuracy: 0.6378 - val_loss: 1.5394 - val_accuracy: 0.0000e+00\n",
      "Epoch 93/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0584 - accuracy: 0.7795 - val_loss: 1.5405 - val_accuracy: 0.0000e+00\n",
      "Epoch 94/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0583 - accuracy: 0.6929 - val_loss: 1.5407 - val_accuracy: 0.0000e+00\n",
      "Epoch 95/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0589 - accuracy: 0.6142 - val_loss: 1.5416 - val_accuracy: 0.0000e+00\n",
      "Epoch 96/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0585 - accuracy: 0.6220 - val_loss: 1.5415 - val_accuracy: 0.0000e+00\n",
      "Epoch 97/500\n",
      "127/127 [==============================] - 0s 79us/step - loss: 1.0585 - accuracy: 0.5669 - val_loss: 1.5417 - val_accuracy: 0.0000e+00\n",
      "Epoch 98/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0589 - accuracy: 0.6457 - val_loss: 1.5421 - val_accuracy: 0.0000e+00\n",
      "Epoch 99/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0580 - accuracy: 0.7795 - val_loss: 1.5419 - val_accuracy: 0.0000e+00\n",
      "Epoch 100/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0580 - accuracy: 0.6299 - val_loss: 1.5415 - val_accuracy: 0.0000e+00\n",
      "Epoch 101/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0582 - accuracy: 0.7087 - val_loss: 1.5424 - val_accuracy: 0.0000e+00\n",
      "Epoch 102/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0576 - accuracy: 0.7874 - val_loss: 1.5426 - val_accuracy: 0.0000e+00\n",
      "Epoch 103/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0586 - accuracy: 0.5354 - val_loss: 1.5419 - val_accuracy: 0.0000e+00\n",
      "Epoch 104/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0577 - accuracy: 0.7402 - val_loss: 1.5414 - val_accuracy: 0.0000e+00\n",
      "Epoch 105/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0582 - accuracy: 0.4803 - val_loss: 1.5417 - val_accuracy: 0.0000e+00\n",
      "Epoch 106/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0580 - accuracy: 0.6614 - val_loss: 1.5414 - val_accuracy: 0.0000e+00\n",
      "Epoch 107/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0577 - accuracy: 0.6535 - val_loss: 1.5408 - val_accuracy: 0.0000e+00\n",
      "Epoch 108/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0576 - accuracy: 0.7087 - val_loss: 1.5407 - val_accuracy: 0.0000e+00\n",
      "Epoch 109/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0576 - accuracy: 0.7874 - val_loss: 1.5404 - val_accuracy: 0.0000e+00\n",
      "Epoch 110/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0574 - accuracy: 0.7874 - val_loss: 1.5408 - val_accuracy: 0.0000e+00\n",
      "Epoch 111/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0583 - accuracy: 0.5906 - val_loss: 1.5419 - val_accuracy: 0.0000e+00\n",
      "Epoch 112/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0576 - accuracy: 0.7638 - val_loss: 1.5414 - val_accuracy: 0.0000e+00\n",
      "Epoch 113/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0574 - accuracy: 0.7087 - val_loss: 1.5418 - val_accuracy: 0.0000e+00\n",
      "Epoch 114/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0577 - accuracy: 0.6220 - val_loss: 1.5408 - val_accuracy: 0.0000e+00\n",
      "Epoch 115/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0571 - accuracy: 0.7795 - val_loss: 1.5407 - val_accuracy: 0.0000e+00\n",
      "Epoch 116/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0578 - accuracy: 0.7087 - val_loss: 1.5413 - val_accuracy: 0.0000e+00\n",
      "Epoch 117/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0584 - accuracy: 0.5906 - val_loss: 1.5415 - val_accuracy: 0.0000e+00\n",
      "Epoch 118/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0579 - accuracy: 0.5984 - val_loss: 1.5416 - val_accuracy: 0.0000e+00\n",
      "Epoch 119/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0573 - accuracy: 0.7638 - val_loss: 1.5424 - val_accuracy: 0.0000e+00\n",
      "Epoch 120/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0572 - accuracy: 0.5984 - val_loss: 1.5425 - val_accuracy: 0.0000e+00\n",
      "Epoch 121/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0578 - accuracy: 0.4961 - val_loss: 1.5434 - val_accuracy: 0.0000e+00\n",
      "Epoch 122/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0576 - accuracy: 0.6457 - val_loss: 1.5427 - val_accuracy: 0.0000e+00\n",
      "Epoch 123/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0570 - accuracy: 0.5039 - val_loss: 1.5426 - val_accuracy: 0.0000e+00\n",
      "Epoch 124/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0568 - accuracy: 0.7874 - val_loss: 1.5426 - val_accuracy: 0.0000e+00\n",
      "Epoch 125/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0575 - accuracy: 0.5512 - val_loss: 1.5430 - val_accuracy: 0.0000e+00\n",
      "Epoch 126/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0570 - accuracy: 0.6850 - val_loss: 1.5429 - val_accuracy: 0.0000e+00\n",
      "Epoch 127/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0570 - accuracy: 0.7402 - val_loss: 1.5419 - val_accuracy: 0.0000e+00\n",
      "Epoch 128/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0571 - accuracy: 0.6614 - val_loss: 1.5415 - val_accuracy: 0.0000e+00\n",
      "Epoch 129/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0578 - accuracy: 0.5748 - val_loss: 1.5403 - val_accuracy: 0.0000e+00\n",
      "Epoch 130/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0568 - accuracy: 0.7717 - val_loss: 1.5399 - val_accuracy: 0.0000e+00\n",
      "Epoch 131/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0566 - accuracy: 0.7717 - val_loss: 1.5400 - val_accuracy: 0.0000e+00\n",
      "Epoch 132/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0568 - accuracy: 0.6378 - val_loss: 1.5395 - val_accuracy: 0.0000e+00\n",
      "Epoch 133/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0567 - accuracy: 0.7874 - val_loss: 1.5392 - val_accuracy: 0.0000e+00\n",
      "Epoch 134/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0563 - accuracy: 0.6220 - val_loss: 1.5398 - val_accuracy: 0.0000e+00\n",
      "Epoch 135/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0562 - accuracy: 0.7638 - val_loss: 1.5403 - val_accuracy: 0.0000e+00\n",
      "Epoch 136/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0563 - accuracy: 0.7480 - val_loss: 1.5403 - val_accuracy: 0.0000e+00\n",
      "Epoch 137/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0563 - accuracy: 0.7008 - val_loss: 1.5399 - val_accuracy: 0.0000e+00\n",
      "Epoch 138/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0563 - accuracy: 0.7874 - val_loss: 1.5394 - val_accuracy: 0.0000e+00\n",
      "Epoch 139/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0561 - accuracy: 0.5512 - val_loss: 1.5395 - val_accuracy: 0.0000e+00\n",
      "Epoch 140/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0562 - accuracy: 0.5276 - val_loss: 1.5396 - val_accuracy: 0.0000e+00\n",
      "Epoch 141/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0563 - accuracy: 0.7717 - val_loss: 1.5392 - val_accuracy: 0.0000e+00\n",
      "Epoch 142/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0568 - accuracy: 0.5197 - val_loss: 1.5394 - val_accuracy: 0.0000e+00\n",
      "Epoch 143/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0558 - accuracy: 0.7874 - val_loss: 1.5397 - val_accuracy: 0.0000e+00\n",
      "Epoch 144/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0563 - accuracy: 0.5039 - val_loss: 1.5398 - val_accuracy: 0.0000e+00\n",
      "Epoch 145/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0558 - accuracy: 0.7874 - val_loss: 1.5393 - val_accuracy: 0.0000e+00\n",
      "Epoch 146/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0559 - accuracy: 0.7638 - val_loss: 1.5390 - val_accuracy: 0.0000e+00\n",
      "Epoch 147/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0559 - accuracy: 0.7795 - val_loss: 1.5384 - val_accuracy: 0.0000e+00\n",
      "Epoch 148/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0567 - accuracy: 0.6220 - val_loss: 1.5387 - val_accuracy: 0.0000e+00\n",
      "Epoch 149/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0556 - accuracy: 0.7874 - val_loss: 1.5391 - val_accuracy: 0.0000e+00\n",
      "Epoch 150/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0560 - accuracy: 0.7165 - val_loss: 1.5391 - val_accuracy: 0.0000e+00\n",
      "Epoch 151/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0560 - accuracy: 0.7008 - val_loss: 1.5381 - val_accuracy: 0.0000e+00\n",
      "Epoch 152/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0558 - accuracy: 0.7717 - val_loss: 1.5374 - val_accuracy: 0.0000e+00\n",
      "Epoch 153/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0557 - accuracy: 0.7795 - val_loss: 1.5370 - val_accuracy: 0.0000e+00\n",
      "Epoch 154/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0557 - accuracy: 0.6850 - val_loss: 1.5370 - val_accuracy: 0.0000e+00\n",
      "Epoch 155/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0559 - accuracy: 0.5748 - val_loss: 1.5379 - val_accuracy: 0.0000e+00\n",
      "Epoch 156/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0556 - accuracy: 0.7795 - val_loss: 1.5377 - val_accuracy: 0.0000e+00\n",
      "Epoch 157/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0558 - accuracy: 0.7559 - val_loss: 1.5388 - val_accuracy: 0.0000e+00\n",
      "Epoch 158/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0551 - accuracy: 0.7795 - val_loss: 1.5387 - val_accuracy: 0.0000e+00\n",
      "Epoch 159/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0554 - accuracy: 0.6929 - val_loss: 1.5394 - val_accuracy: 0.0000e+00\n",
      "Epoch 160/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0554 - accuracy: 0.6772 - val_loss: 1.5397 - val_accuracy: 0.0000e+00\n",
      "Epoch 161/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0566 - accuracy: 0.6063 - val_loss: 1.5390 - val_accuracy: 0.0000e+00\n",
      "Epoch 162/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0549 - accuracy: 0.7874 - val_loss: 1.5393 - val_accuracy: 0.0000e+00\n",
      "Epoch 163/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0552 - accuracy: 0.6850 - val_loss: 1.5390 - val_accuracy: 0.0000e+00\n",
      "Epoch 164/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0553 - accuracy: 0.7638 - val_loss: 1.5386 - val_accuracy: 0.0000e+00\n",
      "Epoch 165/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0555 - accuracy: 0.6772 - val_loss: 1.5381 - val_accuracy: 0.0000e+00\n",
      "Epoch 166/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0548 - accuracy: 0.7717 - val_loss: 1.5385 - val_accuracy: 0.0000e+00\n",
      "Epoch 167/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0549 - accuracy: 0.7165 - val_loss: 1.5386 - val_accuracy: 0.0000e+00\n",
      "Epoch 168/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0551 - accuracy: 0.7165 - val_loss: 1.5394 - val_accuracy: 0.0000e+00\n",
      "Epoch 169/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0550 - accuracy: 0.7480 - val_loss: 1.5388 - val_accuracy: 0.0000e+00\n",
      "Epoch 170/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0560 - accuracy: 0.6378 - val_loss: 1.5378 - val_accuracy: 0.0000e+00\n",
      "Epoch 171/500\n",
      "127/127 [==============================] - 0s 24us/step - loss: 1.0548 - accuracy: 0.7874 - val_loss: 1.5373 - val_accuracy: 0.0000e+00\n",
      "Epoch 172/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0549 - accuracy: 0.6850 - val_loss: 1.5373 - val_accuracy: 0.0000e+00\n",
      "Epoch 173/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0550 - accuracy: 0.6772 - val_loss: 1.5372 - val_accuracy: 0.0000e+00\n",
      "Epoch 174/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0547 - accuracy: 0.7559 - val_loss: 1.5373 - val_accuracy: 0.0000e+00\n",
      "Epoch 175/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0551 - accuracy: 0.7165 - val_loss: 1.5368 - val_accuracy: 0.0000e+00\n",
      "Epoch 176/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0544 - accuracy: 0.7480 - val_loss: 1.5371 - val_accuracy: 0.0000e+00\n",
      "Epoch 177/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0549 - accuracy: 0.5906 - val_loss: 1.5368 - val_accuracy: 0.0000e+00\n",
      "Epoch 178/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0547 - accuracy: 0.7087 - val_loss: 1.5368 - val_accuracy: 0.0000e+00\n",
      "Epoch 179/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0542 - accuracy: 0.7874 - val_loss: 1.5368 - val_accuracy: 0.0000e+00\n",
      "Epoch 180/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0551 - accuracy: 0.5906 - val_loss: 1.5368 - val_accuracy: 0.0000e+00\n",
      "Epoch 181/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0543 - accuracy: 0.7244 - val_loss: 1.5364 - val_accuracy: 0.0000e+00\n",
      "Epoch 182/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0538 - accuracy: 0.7874 - val_loss: 1.5366 - val_accuracy: 0.0000e+00\n",
      "Epoch 183/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0543 - accuracy: 0.7244 - val_loss: 1.5373 - val_accuracy: 0.0000e+00\n",
      "Epoch 184/500\n",
      "127/127 [==============================] - 0s 86us/step - loss: 1.0538 - accuracy: 0.7795 - val_loss: 1.5373 - val_accuracy: 0.0000e+00\n",
      "Epoch 185/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0541 - accuracy: 0.6063 - val_loss: 1.5376 - val_accuracy: 0.0000e+00\n",
      "Epoch 186/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0554 - accuracy: 0.6535 - val_loss: 1.5377 - val_accuracy: 0.0000e+00\n",
      "Epoch 187/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0548 - accuracy: 0.6063 - val_loss: 1.5376 - val_accuracy: 0.0000e+00\n",
      "Epoch 188/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0542 - accuracy: 0.7087 - val_loss: 1.5373 - val_accuracy: 0.0000e+00\n",
      "Epoch 189/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0535 - accuracy: 0.7874 - val_loss: 1.5371 - val_accuracy: 0.0000e+00\n",
      "Epoch 190/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0545 - accuracy: 0.5276 - val_loss: 1.5368 - val_accuracy: 0.0000e+00\n",
      "Epoch 191/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0540 - accuracy: 0.6929 - val_loss: 1.5368 - val_accuracy: 0.0000e+00\n",
      "Epoch 192/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0536 - accuracy: 0.7795 - val_loss: 1.5370 - val_accuracy: 0.0000e+00\n",
      "Epoch 193/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0535 - accuracy: 0.7874 - val_loss: 1.5365 - val_accuracy: 0.0000e+00\n",
      "Epoch 194/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0542 - accuracy: 0.7087 - val_loss: 1.5360 - val_accuracy: 0.0000e+00\n",
      "Epoch 195/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0539 - accuracy: 0.7874 - val_loss: 1.5372 - val_accuracy: 0.0000e+00\n",
      "Epoch 196/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0539 - accuracy: 0.6614 - val_loss: 1.5380 - val_accuracy: 0.0000e+00\n",
      "Epoch 197/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0534 - accuracy: 0.7795 - val_loss: 1.5376 - val_accuracy: 0.0000e+00\n",
      "Epoch 198/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0537 - accuracy: 0.7638 - val_loss: 1.5371 - val_accuracy: 0.0000e+00\n",
      "Epoch 199/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0533 - accuracy: 0.7874 - val_loss: 1.5376 - val_accuracy: 0.0000e+00\n",
      "Epoch 200/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0531 - accuracy: 0.7874 - val_loss: 1.5373 - val_accuracy: 0.0000e+00\n",
      "Epoch 201/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0531 - accuracy: 0.7874 - val_loss: 1.5372 - val_accuracy: 0.0000e+00\n",
      "Epoch 202/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0529 - accuracy: 0.7874 - val_loss: 1.5368 - val_accuracy: 0.0000e+00\n",
      "Epoch 203/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0535 - accuracy: 0.5827 - val_loss: 1.5366 - val_accuracy: 0.0000e+00\n",
      "Epoch 204/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0531 - accuracy: 0.7795 - val_loss: 1.5364 - val_accuracy: 0.0000e+00\n",
      "Epoch 205/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0537 - accuracy: 0.6063 - val_loss: 1.5366 - val_accuracy: 0.0000e+00\n",
      "Epoch 206/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0527 - accuracy: 0.7874 - val_loss: 1.5364 - val_accuracy: 0.0000e+00\n",
      "Epoch 207/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0530 - accuracy: 0.7795 - val_loss: 1.5356 - val_accuracy: 0.0000e+00\n",
      "Epoch 208/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0531 - accuracy: 0.6772 - val_loss: 1.5358 - val_accuracy: 0.0000e+00\n",
      "Epoch 209/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0530 - accuracy: 0.6220 - val_loss: 1.5357 - val_accuracy: 0.0000e+00\n",
      "Epoch 210/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0530 - accuracy: 0.7795 - val_loss: 1.5356 - val_accuracy: 0.0000e+00\n",
      "Epoch 211/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0531 - accuracy: 0.7402 - val_loss: 1.5365 - val_accuracy: 0.0000e+00\n",
      "Epoch 212/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0531 - accuracy: 0.7008 - val_loss: 1.5374 - val_accuracy: 0.0000e+00\n",
      "Epoch 213/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0532 - accuracy: 0.5354 - val_loss: 1.5383 - val_accuracy: 0.0000e+00\n",
      "Epoch 214/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0527 - accuracy: 0.7874 - val_loss: 1.5382 - val_accuracy: 0.0000e+00\n",
      "Epoch 215/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0526 - accuracy: 0.7165 - val_loss: 1.5372 - val_accuracy: 0.0000e+00\n",
      "Epoch 216/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0527 - accuracy: 0.7717 - val_loss: 1.5376 - val_accuracy: 0.0000e+00\n",
      "Epoch 217/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0534 - accuracy: 0.6614 - val_loss: 1.5369 - val_accuracy: 0.0000e+00\n",
      "Epoch 218/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0530 - accuracy: 0.5354 - val_loss: 1.5359 - val_accuracy: 0.0000e+00\n",
      "Epoch 219/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0531 - accuracy: 0.7717 - val_loss: 1.5359 - val_accuracy: 0.0000e+00\n",
      "Epoch 220/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0528 - accuracy: 0.7874 - val_loss: 1.5367 - val_accuracy: 0.0000e+00\n",
      "Epoch 221/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0522 - accuracy: 0.7874 - val_loss: 1.5371 - val_accuracy: 0.0000e+00\n",
      "Epoch 222/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0524 - accuracy: 0.7795 - val_loss: 1.5367 - val_accuracy: 0.0000e+00\n",
      "Epoch 223/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0522 - accuracy: 0.6378 - val_loss: 1.5372 - val_accuracy: 0.0000e+00\n",
      "Epoch 224/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0518 - accuracy: 0.7795 - val_loss: 1.5373 - val_accuracy: 0.0000e+00\n",
      "Epoch 225/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0518 - accuracy: 0.7874 - val_loss: 1.5382 - val_accuracy: 0.0000e+00\n",
      "Epoch 226/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0516 - accuracy: 0.7874 - val_loss: 1.5380 - val_accuracy: 0.0000e+00\n",
      "Epoch 227/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0518 - accuracy: 0.7323 - val_loss: 1.5376 - val_accuracy: 0.0000e+00\n",
      "Epoch 228/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0519 - accuracy: 0.6929 - val_loss: 1.5371 - val_accuracy: 0.0000e+00\n",
      "Epoch 229/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0515 - accuracy: 0.7717 - val_loss: 1.5365 - val_accuracy: 0.0000e+00\n",
      "Epoch 230/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0516 - accuracy: 0.7874 - val_loss: 1.5363 - val_accuracy: 0.0000e+00\n",
      "Epoch 231/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0522 - accuracy: 0.7795 - val_loss: 1.5352 - val_accuracy: 0.0000e+00\n",
      "Epoch 232/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0518 - accuracy: 0.6693 - val_loss: 1.5347 - val_accuracy: 0.0000e+00\n",
      "Epoch 233/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0511 - accuracy: 0.7795 - val_loss: 1.5350 - val_accuracy: 0.0000e+00\n",
      "Epoch 234/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0514 - accuracy: 0.7874 - val_loss: 1.5357 - val_accuracy: 0.0000e+00\n",
      "Epoch 235/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0510 - accuracy: 0.7480 - val_loss: 1.5356 - val_accuracy: 0.0000e+00\n",
      "Epoch 236/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0520 - accuracy: 0.7244 - val_loss: 1.5358 - val_accuracy: 0.0000e+00\n",
      "Epoch 237/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0510 - accuracy: 0.7874 - val_loss: 1.5356 - val_accuracy: 0.0000e+00\n",
      "Epoch 238/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0517 - accuracy: 0.7402 - val_loss: 1.5350 - val_accuracy: 0.0000e+00\n",
      "Epoch 239/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0516 - accuracy: 0.7717 - val_loss: 1.5349 - val_accuracy: 0.0000e+00\n",
      "Epoch 240/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0509 - accuracy: 0.6378 - val_loss: 1.5348 - val_accuracy: 0.0000e+00\n",
      "Epoch 241/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0510 - accuracy: 0.7008 - val_loss: 1.5351 - val_accuracy: 0.0000e+00\n",
      "Epoch 242/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0516 - accuracy: 0.7559 - val_loss: 1.5357 - val_accuracy: 0.0000e+00\n",
      "Epoch 243/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0513 - accuracy: 0.6378 - val_loss: 1.5355 - val_accuracy: 0.0000e+00\n",
      "Epoch 244/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0509 - accuracy: 0.7874 - val_loss: 1.5362 - val_accuracy: 0.0000e+00\n",
      "Epoch 245/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0512 - accuracy: 0.7165 - val_loss: 1.5358 - val_accuracy: 0.0000e+00\n",
      "Epoch 246/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0504 - accuracy: 0.7874 - val_loss: 1.5358 - val_accuracy: 0.0000e+00\n",
      "Epoch 247/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0505 - accuracy: 0.7874 - val_loss: 1.5365 - val_accuracy: 0.0000e+00\n",
      "Epoch 248/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0504 - accuracy: 0.7795 - val_loss: 1.5363 - val_accuracy: 0.0000e+00\n",
      "Epoch 249/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0508 - accuracy: 0.6850 - val_loss: 1.5351 - val_accuracy: 0.0000e+00\n",
      "Epoch 250/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0504 - accuracy: 0.7402 - val_loss: 1.5351 - val_accuracy: 0.0000e+00\n",
      "Epoch 251/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 1.0505 - accuracy: 0.6299 - val_loss: 1.5344 - val_accuracy: 0.0000e+00\n",
      "Epoch 252/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0504 - accuracy: 0.7874 - val_loss: 1.5349 - val_accuracy: 0.0000e+00\n",
      "Epoch 253/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0507 - accuracy: 0.7874 - val_loss: 1.5341 - val_accuracy: 0.0000e+00\n",
      "Epoch 254/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0502 - accuracy: 0.6535 - val_loss: 1.5339 - val_accuracy: 0.0000e+00\n",
      "Epoch 255/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0508 - accuracy: 0.7323 - val_loss: 1.5339 - val_accuracy: 0.0000e+00\n",
      "Epoch 256/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0504 - accuracy: 0.6772 - val_loss: 1.5347 - val_accuracy: 0.0000e+00\n",
      "Epoch 257/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0499 - accuracy: 0.7874 - val_loss: 1.5342 - val_accuracy: 0.0000e+00\n",
      "Epoch 258/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0499 - accuracy: 0.7874 - val_loss: 1.5351 - val_accuracy: 0.0000e+00\n",
      "Epoch 259/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0503 - accuracy: 0.6850 - val_loss: 1.5349 - val_accuracy: 0.0000e+00\n",
      "Epoch 260/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0507 - accuracy: 0.7717 - val_loss: 1.5350 - val_accuracy: 0.0000e+00\n",
      "Epoch 261/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0498 - accuracy: 0.7874 - val_loss: 1.5347 - val_accuracy: 0.0000e+00\n",
      "Epoch 262/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 1.0499 - accuracy: 0.7717 - val_loss: 1.5350 - val_accuracy: 0.0000e+00\n",
      "Epoch 263/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0498 - accuracy: 0.7638 - val_loss: 1.5346 - val_accuracy: 0.0000e+00\n",
      "Epoch 264/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 1.0495 - accuracy: 0.7874 - val_loss: 1.5335 - val_accuracy: 0.0000e+00\n",
      "Epoch 265/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0495 - accuracy: 0.7323 - val_loss: 1.5337 - val_accuracy: 0.0000e+00\n",
      "Epoch 266/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0496 - accuracy: 0.6378 - val_loss: 1.5339 - val_accuracy: 0.0000e+00\n",
      "Epoch 267/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0498 - accuracy: 0.7244 - val_loss: 1.5348 - val_accuracy: 0.0000e+00\n",
      "Epoch 268/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0491 - accuracy: 0.7638 - val_loss: 1.5344 - val_accuracy: 0.0000e+00\n",
      "Epoch 269/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0494 - accuracy: 0.7795 - val_loss: 1.5342 - val_accuracy: 0.0000e+00\n",
      "Epoch 270/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0494 - accuracy: 0.7480 - val_loss: 1.5338 - val_accuracy: 0.0000e+00\n",
      "Epoch 271/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0492 - accuracy: 0.7717 - val_loss: 1.5328 - val_accuracy: 0.0000e+00\n",
      "Epoch 272/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0489 - accuracy: 0.7795 - val_loss: 1.5332 - val_accuracy: 0.0000e+00\n",
      "Epoch 273/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0497 - accuracy: 0.6772 - val_loss: 1.5330 - val_accuracy: 0.0000e+00\n",
      "Epoch 274/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0486 - accuracy: 0.7874 - val_loss: 1.5324 - val_accuracy: 0.0000e+00\n",
      "Epoch 275/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0488 - accuracy: 0.7874 - val_loss: 1.5323 - val_accuracy: 0.0000e+00\n",
      "Epoch 276/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 1.0492 - accuracy: 0.6614 - val_loss: 1.5324 - val_accuracy: 0.0000e+00\n",
      "Epoch 277/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0490 - accuracy: 0.7795 - val_loss: 1.5324 - val_accuracy: 0.0000e+00\n",
      "Epoch 278/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0487 - accuracy: 0.7717 - val_loss: 1.5322 - val_accuracy: 0.0000e+00\n",
      "Epoch 279/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0485 - accuracy: 0.7874 - val_loss: 1.5323 - val_accuracy: 0.0000e+00\n",
      "Epoch 280/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0483 - accuracy: 0.7559 - val_loss: 1.5325 - val_accuracy: 0.0000e+00\n",
      "Epoch 281/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0483 - accuracy: 0.7874 - val_loss: 1.5333 - val_accuracy: 0.0000e+00\n",
      "Epoch 282/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0490 - accuracy: 0.7874 - val_loss: 1.5328 - val_accuracy: 0.0000e+00\n",
      "Epoch 283/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0484 - accuracy: 0.7874 - val_loss: 1.5325 - val_accuracy: 0.0000e+00\n",
      "Epoch 284/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0490 - accuracy: 0.7402 - val_loss: 1.5329 - val_accuracy: 0.0000e+00\n",
      "Epoch 285/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 1.0484 - accuracy: 0.6693 - val_loss: 1.5326 - val_accuracy: 0.0000e+00\n",
      "Epoch 286/500\n",
      "127/127 [==============================] - 0s 79us/step - loss: 1.0481 - accuracy: 0.7717 - val_loss: 1.5320 - val_accuracy: 0.0000e+00\n",
      "Epoch 287/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0491 - accuracy: 0.7402 - val_loss: 1.5329 - val_accuracy: 0.0000e+00\n",
      "Epoch 288/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0479 - accuracy: 0.7874 - val_loss: 1.5333 - val_accuracy: 0.0000e+00\n",
      "Epoch 289/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0477 - accuracy: 0.7874 - val_loss: 1.5327 - val_accuracy: 0.0000e+00\n",
      "Epoch 290/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0477 - accuracy: 0.7874 - val_loss: 1.5321 - val_accuracy: 0.0000e+00\n",
      "Epoch 291/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0480 - accuracy: 0.7874 - val_loss: 1.5317 - val_accuracy: 0.0000e+00\n",
      "Epoch 292/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0485 - accuracy: 0.7874 - val_loss: 1.5321 - val_accuracy: 0.0000e+00\n",
      "Epoch 293/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0474 - accuracy: 0.7244 - val_loss: 1.5320 - val_accuracy: 0.0000e+00\n",
      "Epoch 294/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0471 - accuracy: 0.7874 - val_loss: 1.5319 - val_accuracy: 0.0000e+00\n",
      "Epoch 295/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0473 - accuracy: 0.7874 - val_loss: 1.5316 - val_accuracy: 0.0000e+00\n",
      "Epoch 296/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0473 - accuracy: 0.7874 - val_loss: 1.5314 - val_accuracy: 0.0000e+00\n",
      "Epoch 297/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0473 - accuracy: 0.7638 - val_loss: 1.5317 - val_accuracy: 0.0000e+00\n",
      "Epoch 298/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0471 - accuracy: 0.7874 - val_loss: 1.5318 - val_accuracy: 0.0000e+00\n",
      "Epoch 299/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0472 - accuracy: 0.7795 - val_loss: 1.5314 - val_accuracy: 0.0000e+00\n",
      "Epoch 300/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0469 - accuracy: 0.7874 - val_loss: 1.5318 - val_accuracy: 0.0000e+00\n",
      "Epoch 301/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0471 - accuracy: 0.7874 - val_loss: 1.5324 - val_accuracy: 0.0000e+00\n",
      "Epoch 302/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0473 - accuracy: 0.7874 - val_loss: 1.5327 - val_accuracy: 0.0000e+00\n",
      "Epoch 303/500\n",
      "127/127 [==============================] - 0s 32us/step - loss: 1.0465 - accuracy: 0.7874 - val_loss: 1.5324 - val_accuracy: 0.0000e+00\n",
      "Epoch 304/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0468 - accuracy: 0.7638 - val_loss: 1.5323 - val_accuracy: 0.0000e+00\n",
      "Epoch 305/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0468 - accuracy: 0.7559 - val_loss: 1.5318 - val_accuracy: 0.0000e+00\n",
      "Epoch 306/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0474 - accuracy: 0.6929 - val_loss: 1.5319 - val_accuracy: 0.0000e+00\n",
      "Epoch 307/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0469 - accuracy: 0.7638 - val_loss: 1.5323 - val_accuracy: 0.0000e+00\n",
      "Epoch 308/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0472 - accuracy: 0.6614 - val_loss: 1.5316 - val_accuracy: 0.0000e+00\n",
      "Epoch 309/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0462 - accuracy: 0.7874 - val_loss: 1.5316 - val_accuracy: 0.0000e+00\n",
      "Epoch 310/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0461 - accuracy: 0.7874 - val_loss: 1.5306 - val_accuracy: 0.0000e+00\n",
      "Epoch 311/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0463 - accuracy: 0.7874 - val_loss: 1.5309 - val_accuracy: 0.0000e+00\n",
      "Epoch 312/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0463 - accuracy: 0.7717 - val_loss: 1.5318 - val_accuracy: 0.0000e+00\n",
      "Epoch 313/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0463 - accuracy: 0.7874 - val_loss: 1.5309 - val_accuracy: 0.0000e+00\n",
      "Epoch 314/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0464 - accuracy: 0.7795 - val_loss: 1.5310 - val_accuracy: 0.0000e+00\n",
      "Epoch 315/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0461 - accuracy: 0.7402 - val_loss: 1.5313 - val_accuracy: 0.0000e+00\n",
      "Epoch 316/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0464 - accuracy: 0.7717 - val_loss: 1.5310 - val_accuracy: 0.0000e+00\n",
      "Epoch 317/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0467 - accuracy: 0.4882 - val_loss: 1.5307 - val_accuracy: 0.0000e+00\n",
      "Epoch 318/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0458 - accuracy: 0.7874 - val_loss: 1.5311 - val_accuracy: 0.0000e+00\n",
      "Epoch 319/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0456 - accuracy: 0.7874 - val_loss: 1.5304 - val_accuracy: 0.0000e+00\n",
      "Epoch 320/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0455 - accuracy: 0.7874 - val_loss: 1.5300 - val_accuracy: 0.0000e+00\n",
      "Epoch 321/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0454 - accuracy: 0.7717 - val_loss: 1.5304 - val_accuracy: 0.0000e+00\n",
      "Epoch 322/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0457 - accuracy: 0.7087 - val_loss: 1.5294 - val_accuracy: 0.0000e+00\n",
      "Epoch 323/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0452 - accuracy: 0.7559 - val_loss: 1.5299 - val_accuracy: 0.0000e+00\n",
      "Epoch 324/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0452 - accuracy: 0.7874 - val_loss: 1.5305 - val_accuracy: 0.0000e+00\n",
      "Epoch 325/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0461 - accuracy: 0.5984 - val_loss: 1.5298 - val_accuracy: 0.0000e+00\n",
      "Epoch 326/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0446 - accuracy: 0.7874 - val_loss: 1.5296 - val_accuracy: 0.0000e+00\n",
      "Epoch 327/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0452 - accuracy: 0.7874 - val_loss: 1.5293 - val_accuracy: 0.0000e+00\n",
      "Epoch 328/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0449 - accuracy: 0.7244 - val_loss: 1.5296 - val_accuracy: 0.0000e+00\n",
      "Epoch 329/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0444 - accuracy: 0.7874 - val_loss: 1.5298 - val_accuracy: 0.0000e+00\n",
      "Epoch 330/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0451 - accuracy: 0.7638 - val_loss: 1.5298 - val_accuracy: 0.0000e+00\n",
      "Epoch 331/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0445 - accuracy: 0.7874 - val_loss: 1.5291 - val_accuracy: 0.0000e+00\n",
      "Epoch 332/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0444 - accuracy: 0.7638 - val_loss: 1.5289 - val_accuracy: 0.0000e+00\n",
      "Epoch 333/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0443 - accuracy: 0.7874 - val_loss: 1.5288 - val_accuracy: 0.0000e+00\n",
      "Epoch 334/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0445 - accuracy: 0.7874 - val_loss: 1.5278 - val_accuracy: 0.0000e+00\n",
      "Epoch 335/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0444 - accuracy: 0.7874 - val_loss: 1.5280 - val_accuracy: 0.0000e+00\n",
      "Epoch 336/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0440 - accuracy: 0.7717 - val_loss: 1.5284 - val_accuracy: 0.0000e+00\n",
      "Epoch 337/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0441 - accuracy: 0.7244 - val_loss: 1.5287 - val_accuracy: 0.0000e+00\n",
      "Epoch 338/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0438 - accuracy: 0.7874 - val_loss: 1.5288 - val_accuracy: 0.0000e+00\n",
      "Epoch 339/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0445 - accuracy: 0.7795 - val_loss: 1.5285 - val_accuracy: 0.0000e+00\n",
      "Epoch 340/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0438 - accuracy: 0.7874 - val_loss: 1.5278 - val_accuracy: 0.0000e+00\n",
      "Epoch 341/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0437 - accuracy: 0.7480 - val_loss: 1.5278 - val_accuracy: 0.0000e+00\n",
      "Epoch 342/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0443 - accuracy: 0.7165 - val_loss: 1.5276 - val_accuracy: 0.0000e+00\n",
      "Epoch 343/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0438 - accuracy: 0.7874 - val_loss: 1.5280 - val_accuracy: 0.0000e+00\n",
      "Epoch 344/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0434 - accuracy: 0.7795 - val_loss: 1.5286 - val_accuracy: 0.0000e+00\n",
      "Epoch 345/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0434 - accuracy: 0.7874 - val_loss: 1.5281 - val_accuracy: 0.0000e+00\n",
      "Epoch 346/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0438 - accuracy: 0.7874 - val_loss: 1.5279 - val_accuracy: 0.0000e+00\n",
      "Epoch 347/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0432 - accuracy: 0.7795 - val_loss: 1.5285 - val_accuracy: 0.0000e+00\n",
      "Epoch 348/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0431 - accuracy: 0.7874 - val_loss: 1.5292 - val_accuracy: 0.0000e+00\n",
      "Epoch 349/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0426 - accuracy: 0.7874 - val_loss: 1.5292 - val_accuracy: 0.0000e+00\n",
      "Epoch 350/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0429 - accuracy: 0.7874 - val_loss: 1.5288 - val_accuracy: 0.0000e+00\n",
      "Epoch 351/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0428 - accuracy: 0.7874 - val_loss: 1.5287 - val_accuracy: 0.0000e+00\n",
      "Epoch 352/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0426 - accuracy: 0.7638 - val_loss: 1.5281 - val_accuracy: 0.0000e+00\n",
      "Epoch 353/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0428 - accuracy: 0.7874 - val_loss: 1.5280 - val_accuracy: 0.0000e+00\n",
      "Epoch 354/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0431 - accuracy: 0.7874 - val_loss: 1.5274 - val_accuracy: 0.0000e+00\n",
      "Epoch 355/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0431 - accuracy: 0.7087 - val_loss: 1.5271 - val_accuracy: 0.0000e+00\n",
      "Epoch 356/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0426 - accuracy: 0.7480 - val_loss: 1.5271 - val_accuracy: 0.0000e+00\n",
      "Epoch 357/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0427 - accuracy: 0.7874 - val_loss: 1.5263 - val_accuracy: 0.0000e+00\n",
      "Epoch 358/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0418 - accuracy: 0.7874 - val_loss: 1.5264 - val_accuracy: 0.0000e+00\n",
      "Epoch 359/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0425 - accuracy: 0.7874 - val_loss: 1.5267 - val_accuracy: 0.0000e+00\n",
      "Epoch 360/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0428 - accuracy: 0.7874 - val_loss: 1.5269 - val_accuracy: 0.0000e+00\n",
      "Epoch 361/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0418 - accuracy: 0.7874 - val_loss: 1.5266 - val_accuracy: 0.0000e+00\n",
      "Epoch 362/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0418 - accuracy: 0.7874 - val_loss: 1.5272 - val_accuracy: 0.0000e+00\n",
      "Epoch 363/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0415 - accuracy: 0.7874 - val_loss: 1.5276 - val_accuracy: 0.0000e+00\n",
      "Epoch 364/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0415 - accuracy: 0.7874 - val_loss: 1.5268 - val_accuracy: 0.0000e+00\n",
      "Epoch 365/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0417 - accuracy: 0.7638 - val_loss: 1.5265 - val_accuracy: 0.0000e+00\n",
      "Epoch 366/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0413 - accuracy: 0.7874 - val_loss: 1.5266 - val_accuracy: 0.0000e+00\n",
      "Epoch 367/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0426 - accuracy: 0.7717 - val_loss: 1.5275 - val_accuracy: 0.0000e+00\n",
      "Epoch 368/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0410 - accuracy: 0.7874 - val_loss: 1.5275 - val_accuracy: 0.0000e+00\n",
      "Epoch 369/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0411 - accuracy: 0.7874 - val_loss: 1.5266 - val_accuracy: 0.0000e+00\n",
      "Epoch 370/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0410 - accuracy: 0.7874 - val_loss: 1.5265 - val_accuracy: 0.0000e+00\n",
      "Epoch 371/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0413 - accuracy: 0.7874 - val_loss: 1.5256 - val_accuracy: 0.0000e+00\n",
      "Epoch 372/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0408 - accuracy: 0.7874 - val_loss: 1.5253 - val_accuracy: 0.0000e+00\n",
      "Epoch 373/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0412 - accuracy: 0.7795 - val_loss: 1.5257 - val_accuracy: 0.0000e+00\n",
      "Epoch 374/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0412 - accuracy: 0.7874 - val_loss: 1.5253 - val_accuracy: 0.0000e+00\n",
      "Epoch 375/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0416 - accuracy: 0.6457 - val_loss: 1.5247 - val_accuracy: 0.0000e+00\n",
      "Epoch 376/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0404 - accuracy: 0.7795 - val_loss: 1.5242 - val_accuracy: 0.0000e+00\n",
      "Epoch 377/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0405 - accuracy: 0.7874 - val_loss: 1.5246 - val_accuracy: 0.0000e+00\n",
      "Epoch 378/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0401 - accuracy: 0.7874 - val_loss: 1.5244 - val_accuracy: 0.0000e+00\n",
      "Epoch 379/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0404 - accuracy: 0.7874 - val_loss: 1.5232 - val_accuracy: 0.0000e+00\n",
      "Epoch 380/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0401 - accuracy: 0.7638 - val_loss: 1.5223 - val_accuracy: 0.0000e+00\n",
      "Epoch 381/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0399 - accuracy: 0.7795 - val_loss: 1.5216 - val_accuracy: 0.0000e+00\n",
      "Epoch 382/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0399 - accuracy: 0.7717 - val_loss: 1.5223 - val_accuracy: 0.0000e+00\n",
      "Epoch 383/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0394 - accuracy: 0.7874 - val_loss: 1.5222 - val_accuracy: 0.0000e+00\n",
      "Epoch 384/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0398 - accuracy: 0.7874 - val_loss: 1.5220 - val_accuracy: 0.0000e+00\n",
      "Epoch 385/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0394 - accuracy: 0.7874 - val_loss: 1.5227 - val_accuracy: 0.0000e+00\n",
      "Epoch 386/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0400 - accuracy: 0.7638 - val_loss: 1.5237 - val_accuracy: 0.0000e+00\n",
      "Epoch 387/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0392 - accuracy: 0.7874 - val_loss: 1.5238 - val_accuracy: 0.0000e+00\n",
      "Epoch 388/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0391 - accuracy: 0.7874 - val_loss: 1.5240 - val_accuracy: 0.0000e+00\n",
      "Epoch 389/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0392 - accuracy: 0.7717 - val_loss: 1.5240 - val_accuracy: 0.0000e+00\n",
      "Epoch 390/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0392 - accuracy: 0.7402 - val_loss: 1.5239 - val_accuracy: 0.0000e+00\n",
      "Epoch 391/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0389 - accuracy: 0.7717 - val_loss: 1.5239 - val_accuracy: 0.0000e+00\n",
      "Epoch 392/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0395 - accuracy: 0.7717 - val_loss: 1.5238 - val_accuracy: 0.0000e+00\n",
      "Epoch 393/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0387 - accuracy: 0.7874 - val_loss: 1.5245 - val_accuracy: 0.0000e+00\n",
      "Epoch 394/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0387 - accuracy: 0.7402 - val_loss: 1.5243 - val_accuracy: 0.0000e+00\n",
      "Epoch 395/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0388 - accuracy: 0.7795 - val_loss: 1.5239 - val_accuracy: 0.0000e+00\n",
      "Epoch 396/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0390 - accuracy: 0.7874 - val_loss: 1.5240 - val_accuracy: 0.0000e+00\n",
      "Epoch 397/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0381 - accuracy: 0.7874 - val_loss: 1.5242 - val_accuracy: 0.0000e+00\n",
      "Epoch 398/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0385 - accuracy: 0.7402 - val_loss: 1.5234 - val_accuracy: 0.0000e+00\n",
      "Epoch 399/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0379 - accuracy: 0.7795 - val_loss: 1.5233 - val_accuracy: 0.0000e+00\n",
      "Epoch 400/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0381 - accuracy: 0.7638 - val_loss: 1.5239 - val_accuracy: 0.0000e+00\n",
      "Epoch 401/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0375 - accuracy: 0.7874 - val_loss: 1.5235 - val_accuracy: 0.0000e+00\n",
      "Epoch 402/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0381 - accuracy: 0.7480 - val_loss: 1.5235 - val_accuracy: 0.0000e+00\n",
      "Epoch 403/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0374 - accuracy: 0.7874 - val_loss: 1.5237 - val_accuracy: 0.0000e+00\n",
      "Epoch 404/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0373 - accuracy: 0.7874 - val_loss: 1.5236 - val_accuracy: 0.0000e+00\n",
      "Epoch 405/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0375 - accuracy: 0.7874 - val_loss: 1.5233 - val_accuracy: 0.0000e+00\n",
      "Epoch 406/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0371 - accuracy: 0.7874 - val_loss: 1.5235 - val_accuracy: 0.0000e+00\n",
      "Epoch 407/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0378 - accuracy: 0.7795 - val_loss: 1.5231 - val_accuracy: 0.0000e+00\n",
      "Epoch 408/500\n",
      "127/127 [==============================] - 0s 71us/step - loss: 1.0374 - accuracy: 0.7638 - val_loss: 1.5235 - val_accuracy: 0.0000e+00\n",
      "Epoch 409/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0373 - accuracy: 0.7874 - val_loss: 1.5240 - val_accuracy: 0.0000e+00\n",
      "Epoch 410/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0366 - accuracy: 0.7874 - val_loss: 1.5236 - val_accuracy: 0.0000e+00\n",
      "Epoch 411/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0370 - accuracy: 0.7874 - val_loss: 1.5245 - val_accuracy: 0.0000e+00\n",
      "Epoch 412/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0363 - accuracy: 0.7874 - val_loss: 1.5240 - val_accuracy: 0.0000e+00\n",
      "Epoch 413/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0368 - accuracy: 0.7874 - val_loss: 1.5232 - val_accuracy: 0.0000e+00\n",
      "Epoch 414/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0372 - accuracy: 0.7480 - val_loss: 1.5228 - val_accuracy: 0.0000e+00\n",
      "Epoch 415/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0370 - accuracy: 0.7874 - val_loss: 1.5228 - val_accuracy: 0.0000e+00\n",
      "Epoch 416/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0359 - accuracy: 0.7874 - val_loss: 1.5223 - val_accuracy: 0.0000e+00\n",
      "Epoch 417/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0362 - accuracy: 0.7638 - val_loss: 1.5229 - val_accuracy: 0.0000e+00\n",
      "Epoch 418/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0355 - accuracy: 0.7874 - val_loss: 1.5227 - val_accuracy: 0.0000e+00\n",
      "Epoch 419/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0356 - accuracy: 0.7874 - val_loss: 1.5220 - val_accuracy: 0.0000e+00\n",
      "Epoch 420/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0359 - accuracy: 0.7874 - val_loss: 1.5225 - val_accuracy: 0.0000e+00\n",
      "Epoch 421/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0355 - accuracy: 0.7874 - val_loss: 1.5215 - val_accuracy: 0.0000e+00\n",
      "Epoch 422/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0365 - accuracy: 0.6063 - val_loss: 1.5217 - val_accuracy: 0.0000e+00\n",
      "Epoch 423/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0360 - accuracy: 0.7874 - val_loss: 1.5215 - val_accuracy: 0.0000e+00\n",
      "Epoch 424/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0348 - accuracy: 0.7874 - val_loss: 1.5212 - val_accuracy: 0.0000e+00\n",
      "Epoch 425/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0349 - accuracy: 0.7874 - val_loss: 1.5203 - val_accuracy: 0.0000e+00\n",
      "Epoch 426/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0350 - accuracy: 0.7874 - val_loss: 1.5207 - val_accuracy: 0.0000e+00\n",
      "Epoch 427/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0352 - accuracy: 0.7559 - val_loss: 1.5194 - val_accuracy: 0.0000e+00\n",
      "Epoch 428/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0348 - accuracy: 0.7874 - val_loss: 1.5186 - val_accuracy: 0.0000e+00\n",
      "Epoch 429/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0349 - accuracy: 0.7559 - val_loss: 1.5181 - val_accuracy: 0.0000e+00\n",
      "Epoch 430/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0341 - accuracy: 0.7874 - val_loss: 1.5180 - val_accuracy: 0.0000e+00\n",
      "Epoch 431/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0344 - accuracy: 0.7874 - val_loss: 1.5177 - val_accuracy: 0.0000e+00\n",
      "Epoch 432/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0340 - accuracy: 0.7638 - val_loss: 1.5179 - val_accuracy: 0.0000e+00\n",
      "Epoch 433/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0339 - accuracy: 0.7874 - val_loss: 1.5182 - val_accuracy: 0.0000e+00\n",
      "Epoch 434/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0336 - accuracy: 0.7874 - val_loss: 1.5180 - val_accuracy: 0.0000e+00\n",
      "Epoch 435/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0333 - accuracy: 0.7874 - val_loss: 1.5176 - val_accuracy: 0.0000e+00\n",
      "Epoch 436/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0337 - accuracy: 0.7717 - val_loss: 1.5177 - val_accuracy: 0.0000e+00\n",
      "Epoch 437/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0332 - accuracy: 0.7874 - val_loss: 1.5178 - val_accuracy: 0.0000e+00\n",
      "Epoch 438/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0334 - accuracy: 0.7874 - val_loss: 1.5179 - val_accuracy: 0.0000e+00\n",
      "Epoch 439/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0328 - accuracy: 0.7874 - val_loss: 1.5175 - val_accuracy: 0.0000e+00\n",
      "Epoch 440/500\n",
      "127/127 [==============================] - 0s 40us/step - loss: 1.0335 - accuracy: 0.7874 - val_loss: 1.5168 - val_accuracy: 0.0000e+00\n",
      "Epoch 441/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0329 - accuracy: 0.7717 - val_loss: 1.5172 - val_accuracy: 0.0000e+00\n",
      "Epoch 442/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0331 - accuracy: 0.7874 - val_loss: 1.5166 - val_accuracy: 0.0000e+00\n",
      "Epoch 443/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0331 - accuracy: 0.7874 - val_loss: 1.5158 - val_accuracy: 0.0000e+00\n",
      "Epoch 444/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0323 - accuracy: 0.7874 - val_loss: 1.5154 - val_accuracy: 0.0000e+00\n",
      "Epoch 445/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0322 - accuracy: 0.7874 - val_loss: 1.5148 - val_accuracy: 0.0000e+00\n",
      "Epoch 446/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0325 - accuracy: 0.7874 - val_loss: 1.5151 - val_accuracy: 0.0000e+00\n",
      "Epoch 447/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0323 - accuracy: 0.7402 - val_loss: 1.5146 - val_accuracy: 0.0000e+00\n",
      "Epoch 448/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0321 - accuracy: 0.7874 - val_loss: 1.5151 - val_accuracy: 0.0000e+00\n",
      "Epoch 449/500\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0318 - accuracy: 0.7874 - val_loss: 1.5149 - val_accuracy: 0.0000e+00\n",
      "Epoch 450/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0314 - accuracy: 0.7795 - val_loss: 1.5149 - val_accuracy: 0.0000e+00\n",
      "Epoch 451/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0325 - accuracy: 0.7717 - val_loss: 1.5137 - val_accuracy: 0.0000e+00\n",
      "Epoch 452/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0316 - accuracy: 0.7874 - val_loss: 1.5146 - val_accuracy: 0.0000e+00\n",
      "Epoch 453/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0314 - accuracy: 0.7874 - val_loss: 1.5155 - val_accuracy: 0.0000e+00\n",
      "Epoch 454/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0307 - accuracy: 0.7874 - val_loss: 1.5151 - val_accuracy: 0.0000e+00\n",
      "Epoch 455/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0311 - accuracy: 0.7717 - val_loss: 1.5157 - val_accuracy: 0.0000e+00\n",
      "Epoch 456/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0309 - accuracy: 0.7874 - val_loss: 1.5161 - val_accuracy: 0.0000e+00\n",
      "Epoch 457/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0308 - accuracy: 0.7874 - val_loss: 1.5166 - val_accuracy: 0.0000e+00\n",
      "Epoch 458/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0306 - accuracy: 0.7480 - val_loss: 1.5169 - val_accuracy: 0.0000e+00\n",
      "Epoch 459/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0305 - accuracy: 0.7874 - val_loss: 1.5163 - val_accuracy: 0.0000e+00\n",
      "Epoch 460/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0302 - accuracy: 0.7874 - val_loss: 1.5163 - val_accuracy: 0.0000e+00\n",
      "Epoch 461/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0299 - accuracy: 0.7874 - val_loss: 1.5163 - val_accuracy: 0.0000e+00\n",
      "Epoch 462/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0299 - accuracy: 0.7874 - val_loss: 1.5161 - val_accuracy: 0.0000e+00\n",
      "Epoch 463/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0297 - accuracy: 0.7795 - val_loss: 1.5164 - val_accuracy: 0.0000e+00\n",
      "Epoch 464/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0294 - accuracy: 0.7874 - val_loss: 1.5168 - val_accuracy: 0.0000e+00\n",
      "Epoch 465/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0298 - accuracy: 0.7874 - val_loss: 1.5171 - val_accuracy: 0.0000e+00\n",
      "Epoch 466/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0299 - accuracy: 0.7874 - val_loss: 1.5167 - val_accuracy: 0.0000e+00\n",
      "Epoch 467/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0299 - accuracy: 0.7559 - val_loss: 1.5158 - val_accuracy: 0.0000e+00\n",
      "Epoch 468/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0294 - accuracy: 0.7874 - val_loss: 1.5158 - val_accuracy: 0.0000e+00\n",
      "Epoch 469/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0291 - accuracy: 0.7717 - val_loss: 1.5149 - val_accuracy: 0.0000e+00\n",
      "Epoch 470/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0288 - accuracy: 0.7795 - val_loss: 1.5145 - val_accuracy: 0.0000e+00\n",
      "Epoch 471/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0283 - accuracy: 0.7874 - val_loss: 1.5144 - val_accuracy: 0.0000e+00\n",
      "Epoch 472/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0284 - accuracy: 0.7795 - val_loss: 1.5146 - val_accuracy: 0.0000e+00\n",
      "Epoch 473/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0284 - accuracy: 0.7874 - val_loss: 1.5139 - val_accuracy: 0.0000e+00\n",
      "Epoch 474/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0284 - accuracy: 0.7638 - val_loss: 1.5133 - val_accuracy: 0.0000e+00\n",
      "Epoch 475/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0277 - accuracy: 0.7874 - val_loss: 1.5129 - val_accuracy: 0.0000e+00\n",
      "Epoch 476/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0282 - accuracy: 0.7874 - val_loss: 1.5119 - val_accuracy: 0.0000e+00\n",
      "Epoch 477/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0275 - accuracy: 0.7795 - val_loss: 1.5124 - val_accuracy: 0.0000e+00\n",
      "Epoch 478/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0276 - accuracy: 0.7795 - val_loss: 1.5129 - val_accuracy: 0.0000e+00\n",
      "Epoch 479/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0275 - accuracy: 0.7717 - val_loss: 1.5122 - val_accuracy: 0.0000e+00\n",
      "Epoch 480/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0277 - accuracy: 0.7874 - val_loss: 1.5123 - val_accuracy: 0.0000e+00\n",
      "Epoch 481/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0266 - accuracy: 0.7874 - val_loss: 1.5116 - val_accuracy: 0.0000e+00\n",
      "Epoch 482/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0266 - accuracy: 0.7874 - val_loss: 1.5118 - val_accuracy: 0.0000e+00\n",
      "Epoch 483/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0265 - accuracy: 0.7874 - val_loss: 1.5114 - val_accuracy: 0.0000e+00\n",
      "Epoch 484/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0263 - accuracy: 0.7874 - val_loss: 1.5108 - val_accuracy: 0.0000e+00\n",
      "Epoch 485/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0262 - accuracy: 0.7874 - val_loss: 1.5104 - val_accuracy: 0.0000e+00\n",
      "Epoch 486/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0263 - accuracy: 0.7559 - val_loss: 1.5102 - val_accuracy: 0.0000e+00\n",
      "Epoch 487/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0261 - accuracy: 0.7795 - val_loss: 1.5106 - val_accuracy: 0.0000e+00\n",
      "Epoch 488/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0263 - accuracy: 0.7874 - val_loss: 1.5110 - val_accuracy: 0.0000e+00\n",
      "Epoch 489/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0254 - accuracy: 0.7874 - val_loss: 1.5105 - val_accuracy: 0.0000e+00\n",
      "Epoch 490/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0259 - accuracy: 0.7874 - val_loss: 1.5107 - val_accuracy: 0.0000e+00\n",
      "Epoch 491/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0255 - accuracy: 0.7874 - val_loss: 1.5103 - val_accuracy: 0.0000e+00\n",
      "Epoch 492/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0252 - accuracy: 0.7874 - val_loss: 1.5092 - val_accuracy: 0.0000e+00\n",
      "Epoch 493/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0249 - accuracy: 0.7874 - val_loss: 1.5095 - val_accuracy: 0.0000e+00\n",
      "Epoch 494/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0248 - accuracy: 0.7874 - val_loss: 1.5097 - val_accuracy: 0.0000e+00\n",
      "Epoch 495/500\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0243 - accuracy: 0.7874 - val_loss: 1.5099 - val_accuracy: 0.0000e+00\n",
      "Epoch 496/500\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0245 - accuracy: 0.7874 - val_loss: 1.5097 - val_accuracy: 0.0000e+00\n",
      "Epoch 497/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0239 - accuracy: 0.7874 - val_loss: 1.5093 - val_accuracy: 0.0000e+00\n",
      "Epoch 498/500\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0247 - accuracy: 0.7717 - val_loss: 1.5082 - val_accuracy: 0.0000e+00\n",
      "Epoch 499/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0235 - accuracy: 0.7874 - val_loss: 1.5084 - val_accuracy: 0.0000e+00\n",
      "Epoch 500/500\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0236 - accuracy: 0.7874 - val_loss: 1.5079 - val_accuracy: 0.0000e+00\n",
      "Test accuracy : 66.67%\n"
     ]
    }
   ],
   "source": [
    "#改变神经元个数\n",
    "#SGD\n",
    "model2 = Sequential()\n",
    "model2.add(Dense(100,input_dim=4,activation='sigmoid'))\n",
    "# 隐层\n",
    "model2.add(Dense(20, activation='sigmoid',input_dim=100))  # Dense层为中间层\n",
    "# model2.add(Dropout(0.5))\n",
    "model2.add(Dense(20, activation='sigmoid',input_dim=20))  # Dense层为中间层\n",
    "\n",
    "# 输出层\n",
    "model2.add(Dense(3, input_dim=50,activation='softmax'))\n",
    "sgd=optimizers.SGD(learning_rate=0.01)\n",
    "model2.compile(loss='categorical_crossentropy', optimizer=sgd,metrics=['accuracy'])\n",
    "# model2.summary()\n",
    "historysgdc=model2.fit(input_data,correct_data,validation_split=0.15,epochs=500)\n",
    "# ans=model2.predict(input_test)\n",
    "score3=model2.evaluate(input_test,correct_test,verbose=0)\n",
    "print(\"Test accuracy : %.2f%%\" %(score3[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 127 samples, validate on 23 samples\n",
      "Epoch 1/100\n",
      "127/127 [==============================] - 0s 581us/step - loss: 1.0812 - accuracy: 0.3937 - val_loss: 1.4695 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/100\n",
      "127/127 [==============================] - 0s 32us/step - loss: 1.0696 - accuracy: 0.3937 - val_loss: 1.4708 - val_accuracy: 0.0000e+00\n",
      "Epoch 3/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0660 - accuracy: 0.3937 - val_loss: 1.4720 - val_accuracy: 0.0000e+00\n",
      "Epoch 4/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0691 - accuracy: 0.3937 - val_loss: 1.4729 - val_accuracy: 0.0000e+00\n",
      "Epoch 5/100\n",
      "127/127 [==============================] - 0s 40us/step - loss: 1.0738 - accuracy: 0.3937 - val_loss: 1.4739 - val_accuracy: 0.0000e+00\n",
      "Epoch 6/100\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0793 - accuracy: 0.4016 - val_loss: 1.4750 - val_accuracy: 0.0000e+00\n",
      "Epoch 7/100\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0748 - accuracy: 0.4094 - val_loss: 1.4756 - val_accuracy: 0.0000e+00\n",
      "Epoch 8/100\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0765 - accuracy: 0.4252 - val_loss: 1.4761 - val_accuracy: 0.0000e+00\n",
      "Epoch 9/100\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0631 - accuracy: 0.4016 - val_loss: 1.4770 - val_accuracy: 0.0000e+00\n",
      "Epoch 10/100\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0615 - accuracy: 0.4016 - val_loss: 1.4782 - val_accuracy: 0.0000e+00\n",
      "Epoch 11/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0719 - accuracy: 0.4016 - val_loss: 1.4788 - val_accuracy: 0.0000e+00\n",
      "Epoch 12/100\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0836 - accuracy: 0.3701 - val_loss: 1.4798 - val_accuracy: 0.0000e+00\n",
      "Epoch 13/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0649 - accuracy: 0.4094 - val_loss: 1.4803 - val_accuracy: 0.0000e+00\n",
      "Epoch 14/100\n",
      "127/127 [==============================] - 0s 71us/step - loss: 1.0592 - accuracy: 0.4016 - val_loss: 1.4813 - val_accuracy: 0.0000e+00\n",
      "Epoch 15/100\n",
      "127/127 [==============================] - 0s 40us/step - loss: 1.0605 - accuracy: 0.4252 - val_loss: 1.4823 - val_accuracy: 0.0000e+00\n",
      "Epoch 16/100\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0609 - accuracy: 0.3937 - val_loss: 1.4835 - val_accuracy: 0.0000e+00\n",
      "Epoch 17/100\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0555 - accuracy: 0.4016 - val_loss: 1.4847 - val_accuracy: 0.0000e+00\n",
      "Epoch 18/100\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0590 - accuracy: 0.4252 - val_loss: 1.4858 - val_accuracy: 0.0000e+00\n",
      "Epoch 19/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0693 - accuracy: 0.4173 - val_loss: 1.4871 - val_accuracy: 0.0000e+00\n",
      "Epoch 20/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0679 - accuracy: 0.4094 - val_loss: 1.4878 - val_accuracy: 0.0000e+00\n",
      "Epoch 21/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0674 - accuracy: 0.4331 - val_loss: 1.4888 - val_accuracy: 0.0000e+00\n",
      "Epoch 22/100\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0595 - accuracy: 0.4252 - val_loss: 1.4899 - val_accuracy: 0.0000e+00\n",
      "Epoch 23/100\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0717 - accuracy: 0.3622 - val_loss: 1.4909 - val_accuracy: 0.0000e+00\n",
      "Epoch 24/100\n",
      "127/127 [==============================] - 0s 32us/step - loss: 1.0710 - accuracy: 0.4331 - val_loss: 1.4919 - val_accuracy: 0.0000e+00\n",
      "Epoch 25/100\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0699 - accuracy: 0.4567 - val_loss: 1.4922 - val_accuracy: 0.0000e+00\n",
      "Epoch 26/100\n",
      "127/127 [==============================] - 0s 32us/step - loss: 1.0772 - accuracy: 0.3622 - val_loss: 1.4929 - val_accuracy: 0.0000e+00\n",
      "Epoch 27/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0708 - accuracy: 0.4094 - val_loss: 1.4936 - val_accuracy: 0.0000e+00\n",
      "Epoch 28/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0627 - accuracy: 0.3701 - val_loss: 1.4946 - val_accuracy: 0.0000e+00\n",
      "Epoch 29/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0614 - accuracy: 0.3701 - val_loss: 1.4957 - val_accuracy: 0.0000e+00\n",
      "Epoch 30/100\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0713 - accuracy: 0.4409 - val_loss: 1.4962 - val_accuracy: 0.0000e+00\n",
      "Epoch 31/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0553 - accuracy: 0.4803 - val_loss: 1.4967 - val_accuracy: 0.0000e+00\n",
      "Epoch 32/100\n",
      "127/127 [==============================] - 0s 32us/step - loss: 1.0631 - accuracy: 0.3780 - val_loss: 1.4974 - val_accuracy: 0.0000e+00\n",
      "Epoch 33/100\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0621 - accuracy: 0.4252 - val_loss: 1.4984 - val_accuracy: 0.0000e+00\n",
      "Epoch 34/100\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0654 - accuracy: 0.4173 - val_loss: 1.4989 - val_accuracy: 0.0000e+00\n",
      "Epoch 35/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0650 - accuracy: 0.3937 - val_loss: 1.4996 - val_accuracy: 0.0000e+00\n",
      "Epoch 36/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0661 - accuracy: 0.3622 - val_loss: 1.5003 - val_accuracy: 0.0000e+00\n",
      "Epoch 37/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0680 - accuracy: 0.3543 - val_loss: 1.5008 - val_accuracy: 0.0000e+00\n",
      "Epoch 38/100\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0679 - accuracy: 0.3622 - val_loss: 1.5018 - val_accuracy: 0.0000e+00\n",
      "Epoch 39/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0554 - accuracy: 0.4409 - val_loss: 1.5023 - val_accuracy: 0.0000e+00\n",
      "Epoch 40/100\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0580 - accuracy: 0.4803 - val_loss: 1.5032 - val_accuracy: 0.0000e+00\n",
      "Epoch 41/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0687 - accuracy: 0.3622 - val_loss: 1.5038 - val_accuracy: 0.0000e+00\n",
      "Epoch 42/100\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0724 - accuracy: 0.3780 - val_loss: 1.5046 - val_accuracy: 0.0000e+00\n",
      "Epoch 43/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0738 - accuracy: 0.4016 - val_loss: 1.5050 - val_accuracy: 0.0000e+00\n",
      "Epoch 44/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0596 - accuracy: 0.4173 - val_loss: 1.5056 - val_accuracy: 0.0000e+00\n",
      "Epoch 45/100\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0629 - accuracy: 0.4016 - val_loss: 1.5061 - val_accuracy: 0.0000e+00\n",
      "Epoch 46/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0664 - accuracy: 0.4016 - val_loss: 1.5069 - val_accuracy: 0.0000e+00\n",
      "Epoch 47/100\n",
      "127/127 [==============================] - 0s 86us/step - loss: 1.0639 - accuracy: 0.4252 - val_loss: 1.5070 - val_accuracy: 0.0000e+00\n",
      "Epoch 48/100\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0610 - accuracy: 0.4252 - val_loss: 1.5078 - val_accuracy: 0.0000e+00\n",
      "Epoch 49/100\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0676 - accuracy: 0.4173 - val_loss: 1.5082 - val_accuracy: 0.0000e+00\n",
      "Epoch 50/100\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0699 - accuracy: 0.3543 - val_loss: 1.5085 - val_accuracy: 0.0000e+00\n",
      "Epoch 51/100\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0586 - accuracy: 0.4409 - val_loss: 1.5091 - val_accuracy: 0.0000e+00\n",
      "Epoch 52/100\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0539 - accuracy: 0.5039 - val_loss: 1.5097 - val_accuracy: 0.0000e+00\n",
      "Epoch 53/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0565 - accuracy: 0.3937 - val_loss: 1.5104 - val_accuracy: 0.0000e+00\n",
      "Epoch 54/100\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0702 - accuracy: 0.3780 - val_loss: 1.5109 - val_accuracy: 0.0000e+00\n",
      "Epoch 55/100\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0660 - accuracy: 0.4252 - val_loss: 1.5115 - val_accuracy: 0.0000e+00\n",
      "Epoch 56/100\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0631 - accuracy: 0.4331 - val_loss: 1.5120 - val_accuracy: 0.0000e+00\n",
      "Epoch 57/100\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0648 - accuracy: 0.4252 - val_loss: 1.5124 - val_accuracy: 0.0000e+00\n",
      "Epoch 58/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0544 - accuracy: 0.3937 - val_loss: 1.5130 - val_accuracy: 0.0000e+00\n",
      "Epoch 59/100\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0645 - accuracy: 0.3622 - val_loss: 1.5135 - val_accuracy: 0.0000e+00\n",
      "Epoch 60/100\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0548 - accuracy: 0.4173 - val_loss: 1.5142 - val_accuracy: 0.0000e+00\n",
      "Epoch 61/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0686 - accuracy: 0.3386 - val_loss: 1.5148 - val_accuracy: 0.0000e+00\n",
      "Epoch 62/100\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0576 - accuracy: 0.4567 - val_loss: 1.5152 - val_accuracy: 0.0000e+00\n",
      "Epoch 63/100\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0612 - accuracy: 0.3465 - val_loss: 1.5154 - val_accuracy: 0.0000e+00\n",
      "Epoch 64/100\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0700 - accuracy: 0.3780 - val_loss: 1.5154 - val_accuracy: 0.0000e+00\n",
      "Epoch 65/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0559 - accuracy: 0.4567 - val_loss: 1.5161 - val_accuracy: 0.0000e+00\n",
      "Epoch 66/100\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0655 - accuracy: 0.3858 - val_loss: 1.5165 - val_accuracy: 0.0000e+00\n",
      "Epoch 67/100\n",
      "127/127 [==============================] - 0s 70us/step - loss: 1.0579 - accuracy: 0.4409 - val_loss: 1.5169 - val_accuracy: 0.0000e+00\n",
      "Epoch 68/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0682 - accuracy: 0.3543 - val_loss: 1.5175 - val_accuracy: 0.0000e+00\n",
      "Epoch 69/100\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0631 - accuracy: 0.4094 - val_loss: 1.5181 - val_accuracy: 0.0000e+00\n",
      "Epoch 70/100\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0618 - accuracy: 0.4252 - val_loss: 1.5183 - val_accuracy: 0.0000e+00\n",
      "Epoch 71/100\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0649 - accuracy: 0.3937 - val_loss: 1.5188 - val_accuracy: 0.0000e+00\n",
      "Epoch 72/100\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0499 - accuracy: 0.4488 - val_loss: 1.5194 - val_accuracy: 0.0000e+00\n",
      "Epoch 73/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0682 - accuracy: 0.3386 - val_loss: 1.5199 - val_accuracy: 0.0000e+00\n",
      "Epoch 74/100\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0619 - accuracy: 0.4173 - val_loss: 1.5205 - val_accuracy: 0.0000e+00\n",
      "Epoch 75/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0586 - accuracy: 0.4646 - val_loss: 1.5210 - val_accuracy: 0.0000e+00\n",
      "Epoch 76/100\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0732 - accuracy: 0.3701 - val_loss: 1.5215 - val_accuracy: 0.0000e+00\n",
      "Epoch 77/100\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0610 - accuracy: 0.4252 - val_loss: 1.5216 - val_accuracy: 0.0000e+00\n",
      "Epoch 78/100\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0613 - accuracy: 0.4409 - val_loss: 1.5217 - val_accuracy: 0.0000e+00\n",
      "Epoch 79/100\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0609 - accuracy: 0.4173 - val_loss: 1.5220 - val_accuracy: 0.0000e+00\n",
      "Epoch 80/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0483 - accuracy: 0.4409 - val_loss: 1.5228 - val_accuracy: 0.0000e+00\n",
      "Epoch 81/100\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0575 - accuracy: 0.4331 - val_loss: 1.5230 - val_accuracy: 0.0000e+00\n",
      "Epoch 82/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0538 - accuracy: 0.4961 - val_loss: 1.5233 - val_accuracy: 0.0000e+00\n",
      "Epoch 83/100\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0574 - accuracy: 0.4016 - val_loss: 1.5236 - val_accuracy: 0.0000e+00\n",
      "Epoch 84/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0726 - accuracy: 0.3543 - val_loss: 1.5239 - val_accuracy: 0.0000e+00\n",
      "Epoch 85/100\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0592 - accuracy: 0.4724 - val_loss: 1.5242 - val_accuracy: 0.0000e+00\n",
      "Epoch 86/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0712 - accuracy: 0.3780 - val_loss: 1.5241 - val_accuracy: 0.0000e+00\n",
      "Epoch 87/100\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0619 - accuracy: 0.4173 - val_loss: 1.5244 - val_accuracy: 0.0000e+00\n",
      "Epoch 88/100\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0540 - accuracy: 0.4724 - val_loss: 1.5246 - val_accuracy: 0.0000e+00\n",
      "Epoch 89/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0503 - accuracy: 0.4724 - val_loss: 1.5252 - val_accuracy: 0.0000e+00\n",
      "Epoch 90/100\n",
      "127/127 [==============================] - 0s 63us/step - loss: 1.0727 - accuracy: 0.3701 - val_loss: 1.5252 - val_accuracy: 0.0000e+00\n",
      "Epoch 91/100\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0609 - accuracy: 0.4252 - val_loss: 1.5253 - val_accuracy: 0.0000e+00\n",
      "Epoch 92/100\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0503 - accuracy: 0.4961 - val_loss: 1.5258 - val_accuracy: 0.0000e+00\n",
      "Epoch 93/100\n",
      "127/127 [==============================] - 0s 47us/step - loss: 1.0639 - accuracy: 0.4409 - val_loss: 1.5259 - val_accuracy: 0.0000e+00\n",
      "Epoch 94/100\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0542 - accuracy: 0.3937 - val_loss: 1.5264 - val_accuracy: 0.0000e+00\n",
      "Epoch 95/100\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0541 - accuracy: 0.4252 - val_loss: 1.5267 - val_accuracy: 0.0000e+00\n",
      "Epoch 96/100\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0665 - accuracy: 0.4094 - val_loss: 1.5273 - val_accuracy: 0.0000e+00\n",
      "Epoch 97/100\n",
      "127/127 [==============================] - 0s 31us/step - loss: 1.0617 - accuracy: 0.4488 - val_loss: 1.5279 - val_accuracy: 0.0000e+00\n",
      "Epoch 98/100\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0605 - accuracy: 0.4252 - val_loss: 1.5281 - val_accuracy: 0.0000e+00\n",
      "Epoch 99/100\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1.0665 - accuracy: 0.4173 - val_loss: 1.5280 - val_accuracy: 0.0000e+00\n",
      "Epoch 100/100\n",
      "127/127 [==============================] - 0s 55us/step - loss: 1.0589 - accuracy: 0.4252 - val_loss: 1.5284 - val_accuracy: 0.0000e+00\n"
     ]
    }
   ],
   "source": [
    "history02=model2.fit(input_data,correct_data,validation_split=0.15,epochs=100)\n",
    "anssgd_drop=model2.predict(input_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       [0.39577895, 0.39245412, 0.21176687],\n",
       "       [0.3916875 , 0.39796185, 0.2103506 ],\n",
       "       [0.39423963, 0.39445734, 0.21130303],\n",
       "       [0.39542013, 0.39264345, 0.2119364 ],\n",
       "       [0.39493304, 0.3937859 , 0.21128102],\n",
       "       [0.39615503, 0.39207748, 0.21176748],\n",
       "       [0.38936144, 0.40117857, 0.20945993],\n",
       "       [0.3932433 , 0.39572567, 0.211031  ],\n",
       "       [0.39278066, 0.3967362 , 0.21048304],\n",
       "       [0.39265803, 0.3967333 , 0.21060865],\n",
       "       [0.39346293, 0.39514473, 0.21139243],\n",
       "       [0.39518502, 0.39268094, 0.21213403],\n",
       "       [0.39350045, 0.39534083, 0.21115875],\n",
       "       [0.39555827, 0.39279976, 0.211642  ],\n",
       "       [0.39244372, 0.3962807 , 0.21127558],\n",
       "       [0.39085546, 0.39929226, 0.20985234],\n",
       "       [0.39407754, 0.39432877, 0.21159367],\n",
       "       [0.3942441 , 0.39483505, 0.21092084],\n",
       "       [0.39397866, 0.3946903 , 0.21133105],\n",
       "       [0.39639637, 0.39050242, 0.21310127],\n",
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       "       [0.39528656, 0.3908319 , 0.21388154],\n",
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       "       [0.39109737, 0.3972434 , 0.21165925],\n",
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       "       [0.39326406, 0.39330652, 0.21342944],\n",
       "       [0.38907996, 0.39882207, 0.21209791],\n",
       "       [0.3884954 , 0.3992741 , 0.2122305 ],\n",
       "       [0.39484298, 0.39073688, 0.21442018],\n",
       "       [0.39267775, 0.39461005, 0.21271217],\n",
       "       [0.3915089 , 0.39523923, 0.21325183],\n",
       "       [0.39015496, 0.3968628 , 0.21298222],\n",
       "       [0.39146507, 0.39598897, 0.21254593],\n",
       "       [0.39076588, 0.39583865, 0.2133955 ],\n",
       "       [0.3881138 , 0.39959365, 0.21229254],\n",
       "       [0.39276934, 0.39395136, 0.21327928],\n",
       "       [0.38796902, 0.3986499 , 0.21338105],\n",
       "       [0.39144275, 0.39590216, 0.21265498],\n",
       "       [0.3888747 , 0.39848086, 0.21264446],\n",
       "       [0.38764855, 0.39993545, 0.21241592],\n",
       "       [0.38918307, 0.39832094, 0.21249601],\n",
       "       [0.38849223, 0.3989308 , 0.21257703],\n",
       "       [0.39008206, 0.3972456 , 0.2126724 ],\n",
       "       [0.39240626, 0.39504007, 0.21255367]], dtype=float32)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anssgd#纯sgd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "array([[0.3970078 , 0.39126015, 0.21173207],\n",
       "       [0.39786932, 0.38973147, 0.21239929],\n",
       "       [0.39394417, 0.39114943, 0.21490642],\n",
       "       [0.39615157, 0.39097613, 0.21287228],\n",
       "       [0.39685532, 0.39125282, 0.21189186],\n",
       "       [0.39680114, 0.39005086, 0.21314801],\n",
       "       [0.3988647 , 0.38911998, 0.2120153 ],\n",
       "       [0.39221862, 0.39158332, 0.21619804],\n",
       "       [0.3957033 , 0.3910676 , 0.21322915],\n",
       "       [0.39511293, 0.39043644, 0.21445055],\n",
       "       [0.3953543 , 0.39041778, 0.21422787],\n",
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       "       [0.39664367, 0.39155746, 0.21179886],\n",
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       "       [0.38813543, 0.3941737 , 0.21769083],\n",
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       "       [0.3944125 , 0.3887614 , 0.21682604],\n",
       "       [0.39221352, 0.3922023 , 0.21558414],\n",
       "       [0.3890999 , 0.394176  , 0.21672404],\n",
       "       [0.3925009 , 0.3908092 , 0.21668985],\n",
       "       [0.38912624, 0.39516908, 0.21570475],\n",
       "       [0.3875159 , 0.39541912, 0.21706498],\n",
       "       [0.39192477, 0.39255527, 0.21551992],\n",
       "       [0.3880632 , 0.39532566, 0.21661112],\n",
       "       [0.39135173, 0.39150655, 0.21714176],\n",
       "       [0.390016  , 0.39301628, 0.21696769],\n",
       "       [0.39011946, 0.39282727, 0.21705325],\n",
       "       [0.39028922, 0.39172444, 0.21798636],\n",
       "       [0.39076635, 0.392078  , 0.21715559],\n",
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       "       [0.39306134, 0.38988447, 0.21705417]], dtype=float32)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anssgd_drop#sgd+drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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